Note recognition and management using color classification

Technical Field

The present disclosure relates to note content capturing, recognition, extraction, and/ or management tools and systems.

Background

Physical notes have been broadly used in recording, sharing, and communicating ideas and information. For example, during a collaboration session (e.g., brainstorming session), participants write down ideas on Post-It^® notes, whiteboard, or paper, and then share with one another. In addition, people commonly use notes throughout the day to memorialize information or content which the individual does not want to forget. As additional examples, people frequently use notes as reminders of actions or events to take in the future, such as to make a telephone call, revise a document or to fill out a time sheet.

For example, in many situations people participate in a collaboration session by writing information on paper-based notes, such as Post-it^® notes. Paper Post-it^® notes can readily be removed from a dispenser pad of sticky-back paper Post-it^® notes and applied to various surfaces, such as whiteboards, documents, the tops of desks, telephones, or the like. Information can be written on paper Post-it^® notes either before or after the paper Post-it^® notes are detached from their dispenser pad or attached to their target surfaces. Paper Post-it^® notes can be easily moved from one surface to another, such as between documents or between documents and the tops of desks, they can overlap edges or boundaries of documents, they can be layered, and they can be moved with the objects to which they are attached.

Software programs currently exist which permit computer users to create a software-based note in a digital form and to utilize the digital note within a computing environment. For example, a computer user may create a digital note and "attach" the digital note to an electronic document a desktop or electronic workspace presented by the computing environment.

<nplcit id="ncit0001" npl-type="b"><text>K. M. Everitt et al., "Two worlds apart: bridging the gap between physical and virtual media for distributed design collaboration", CHI 2003. CONFERENCE PROCEEDINGS. CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. FORT LAUDERDALE, FL, APRIL 5 - 10, 2003; [CHI CONFERENCE PROCEEDINGS. HUMAN FACTORS IN COMPUTING SYSTEMS], NEW YORK, NY: ACM, US, (20030405), pages 553 - 560</text></nplcit> discloses a remote collaboration system that has two awareness mechanisms, transient ink input for gestures and a blue shadow of the remote collaborator for presence.

<nplcit id="ncit0002" npl-type="b"><text>L. Bian et al., "PalimPost: information convergence using sticky notes", WEB OF THINGS, ACM, 2 PENN PLAZA, SUITE 701 NEW YORK NY 10121-0701 USA, (20110612), pages 1 - 6</text></nplcit> discloses a converged system for storing, searching, and sharing digital and physical world information using sticky notes and mobile devices. The system uses physical sticky notes with embedded QR codes, as well as virtual sticky notes on mobile devices.

<nplcit id="ncit0003" npl-type="b"><text>S. R. Klemmer et al., "The Designers' Outpost: A Tangible Interface for Collaborative Web Site Design", Conference UIST '01: ACM Symposium on User Interface Software and Technology, ACM 2001, 3(2</text></nplcit>) discloses a tangible user interface that combines the affordances of paper and large physical workspaces with the advantages of electronic media to support information design. Users collaboratively author web site information architectures on an electronic whiteboard using physical media (Post-it notes and images), structuring and annotating that information with electronic pens.

<nplcit id="ncit0004" npl-type="b"><text>T. Hurlbutt et al., "Bridging the Gap: Fluidly Connecting Paper Notecards with Digital Representations for Story/Task-Based Planning", CHI 2006, April 22-27, 2006, Montreal, Quebec, Canada. ACM 1-59593-298-4/06/0004</text></nplcit> discloses a prototype of a system that uses digital pen technology to integrate paper notecards and digital task plan representations.

<nplcit id="ncit0005" npl-type="b"><text>D. Soendoro et al., "Traffic sign recognition with Color-based Method, shape-arc estimation and SVM", International Conference on Electrical Engineering and Informatics, ICEEI 2011, Bandung, Indonesia, 17-19 July, 2011, IEEE</text></nplcit> discloses a traffic sign recognition method that uses a combination of a Color-based Method and SVM.

<nplcit id="ncit0006" npl-type="b"><text>QUENTIN STAFFORD-FRASER ET AL: "BrightBoard: a video-augmented environment", HUMAN FACTORS IN COMPUTING SYSTEMS, ACM, NEW YORK, USA, 13 April 1996), pages 134-141</text></nplcit>, discloses a video-based system, designed to enhance the facilities of an ordinary whiteboard, allowing a user to control a computer through marks made on the board. It also discloses the use of an adaptive threshold for separating notes from background.

Summary

In general, the disclosure describes techniques for creating and manipulating software notes representative of physical notes. According to the invention, a method and a note recognition system as recited in the independent claims are provided. The dependent claims define preferred embodiments.

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Brief Description of the Drawings

The accompanying drawings are incorporated in and constitute a part of this specification and, together with the description, explain the advantages and principles of the invention. <ul id="ul0001" list-style="none" compact="compact"><li><figref idref="f0001">Figure 1</figref> is a representation illustrating one example of a user capturing an image of a workspace with notes using an image capture device on a mobile device.</li><li><figref idref="f0002">Figure 2</figref> is a block diagram illustrating one example of a mobile device.</li><li><figref idref="f0003">Figure 3</figref> is a block diagram illustrating one example of a user application to process the input image.</li><li><figref idref="f0004">Figure 4A</figref> illustrates an embodiment of a note recognition system.</li><li><figref idref="f0004">Figure 4B</figref> illustrates a note management system.</li><li><figref idref="f0005 f0006">Figures 5A-5D</figref> illustrate some examples of notes having marks.</li><li><figref idref="f0006">Figures 5E-5F</figref> illustrate some examples of notes with segments.</li><li><figref idref="f0007">Figure 5G</figref> illustrates an example of creating segments of a note using marks.</li><li><figref idref="f0007">Figure 5H</figref> illustrates an example of marks.</li><li><figref idref="f0008">Figure 6A</figref> illustrates a flow diagram of an embodiment of a note recognition and/or management system.</li><li><figref idref="f0008">Figure 6B</figref> illustrates a flow diagram of an embodiment, not covered by the claimed invention, of extracting content of notes.</li><li><figref idref="f0009">Figure 6C</figref> illustrates a flow diagram of authenticating a note.</li><li><figref idref="f0010">Figure 6D</figref> illustrates a functional flow diagram of an embodiment of extracting content of notes or note segments using color classification algorithm.</li><li><figref idref="f0011">Figure 6E</figref> illustrates a functional flow diagram of an example of extracting content of notes or note segments having three different colors using color classification algorithm.</li><li><figref idref="f0012">Figure 6F</figref> illustrates a functional flow diagram of an embodiment, not covered by the claimed invention, of extracting a set of content from a note with segments.</li><li><figref idref="f0013">Figure 6G</figref> illustrations a functional flow diagram of an embodiment, not covered by the claimed invention, of segmenting a note and extracting a set of content for the segments.</li><li><figref idref="f0014">Figures 7A - 7D</figref> illustrate an example of content extraction process of a note with a mark.</li><li><figref idref="f0014 f0015">Figures 8A - 8D</figref> illustrate an embodiment of content extraction of a plurality of notes with retroreflective marks.</li><li><figref idref="f0016">Figure 9</figref> illustrates a module diagram of an embodiment, not covered by the claimed invention, of a note recognition system.</li><li><figref idref="f0016">Figure 10A</figref> illustrates a flow diagram of a note management system.</li><li><figref idref="f0017">Figure 10B</figref> illustrates examples of how a system may label a note.</li><li><figref idref="f0017">Figure 11</figref> illustrates a module diagram of an embodiment, not covered by the claimed invention, of a note management system.</li><li><figref idref="f0018">Figure 12A</figref> illustrates an example of content extraction process of a segmented note with marks.</li><li><figref idref="f0018">Figure 12B</figref> illustrates an example of content extraction process of a note with color segments.</li><li><figref idref="f0019">Figure 12C</figref> illustrates an exemplary scenario of marking a white board to extract segmented content.</li><li><figref idref="f0020">Figures 13A and 13B</figref> illustrate an example of extracting colored notes using color classification algorithm.</li><li><figref idref="f0021">Figure 14</figref> illustrates a graph of classification values vs. colors.</li></ul>

Detailed Description

The present disclosure describes techniques for creating and manipulating software notes representative of physical notes. For example, techniques are described for recognizing physical notes present within a physical environment, capturing information therefrom and creating corresponding digital representations of the physical notes, referred to herein as digital notes or software-based notes. Further, at least some aspects of the present disclosure are directed to techniques for managing multiple notes, such as storing, retrieving, editing the digital notes, categorizing and grouping the digital notes, or the like.

Notes have been broadly used in recording, sharing, and communicating ideas and information. For example, during a collaboration session (e.g., brainstorming session), participants write down ideas on Post-It^™ notes, whiteboard, or paper, and then share with one another. The invention is directed to systems and methods of capturing multiple notes and extracting content of notes. In some embodiments, at least some of the notes include a mark to facilitate the recognition and extraction of the notes. The notes are captured and identified/recognized in one visual representation of a scene. A visual representation of a scene, for example, can be a digital photograph, or still fame from video of note(s) and the surrounding environment. Further, at least some aspects of the present disclosure are directed to systems and methods of managing multiple notes, such as storing and retrieving the notes, categorizing and grouping the notes, or the like. In some cases, the note management system can improve the efficiency in capturing and extracting note content from a large number of notes. In addition, the note management system can improve the efficiency in grouping and managing notes.

In general, notes can include physical notes and digital notes. Physical notes generally refer to objects with a general boundary and recognizable content. Physical notes can include the resulting objects after people write, draw, or enter via other type of inputs on the objects, for example, paper, white board, or other objects accepting the inputs. By way of examples, physical notes can include hand-written Post-It^™ notes, paper, or film, white-board with drawings, posters, and signs. In some cases, physical notes can be generated using digital means, e.g. printing onto printable Post-It^™ notes or printed document. In some cases, one object can include several notes. For example, several ideas can be written on a piece of poster paper or a white-board. In some implementations, to facilitate the recognition of these notes, marks, such as lines, shapes, colors, symbols, markers, or stickers, can be applied to the edges of the notes. Physical notes can be two-dimensional or three dimensional. Physical notes can have various shapes and sizes. For example, a physical note may be a 7.62 x 7.62 cm (3 x 3 inches) note; a physical note may be a 66.04 x 99.06 cm (26 x 39 inches) poster; and a physical note may be a triangular metal sign. In some cases, physical notes have known shapes and/or sizes that conform to standards, such as legal, A3, A4, and other size standards, and known shapes, which may not be limited to geometric shapes, such as stars, circles, rectangles, or the like. In other cases, physical notes may have non-standardized sizes and/or irregular shapes. Digital notes generally refer to digital objects with information and/or ideas. Digital notes can be generated using digital inputs. Digital inputs can include, for example, keyboards, touch screens, digital cameras, digital recording devices, stylus, digital pens, or the like. In some cases, digital notes may be representative of physical notes.

Notes are used in a collaboration space. Collaboration space generally refers to a gathering area allowing more than one person to share ideas and thoughts with each other. The collaboration space can include virtual spaces allowing a group of persons to share ideas and thoughts remotely, besides the gathering area.

<figref idref="f0001">FIG. 1</figref> illustrates an example of a note recognition environment 10. In the example of <figref idref="f0001">FIG. 1</figref>, environment 10 includes a mobile device 15 to capture and recognize one of more notes 22 from a workspace 20. As described herein, mobile device provides an execution environment for one or more software applications that, as described, can efficiently capture and extract note content from a large number of physical notes, such as the collection of notes 22 from workspace 20. In this example, notes 22 may be the results of a collaborative brainstorming session having multiple participants. As described, mobile device 15 and the software executing thereon may perform a variety of note-related operations, including automated creation of digital notes representative of physical notes 22 of workspace 20.

In the example implementation, mobile device 15 includes, among other components, an image capture device 18 and a presentation device 28. In addition, although not shown in <figref idref="f0001">FIG. 1</figref>, mobile device 15 may include one or more processors, microprocessors, internal memory and/or data storage and other electronic circuitry for executing software or firmware to provide the functionality described herein.

In general, image capture device 18 is a camera or other component configured to capture image data representative of workspace 20 and notes 22 positioned therein. In other words, the image data captures a visual representation of an environment, such as workspace 20, having a plurality of physical notes. Although discussed as a camera of mobile device 15, image capture device 18 may comprise other components capable of capturing image data, such as a video recorder, an infrared camera, a CCD (Charge Coupled Device) array, a laser scanner, or the like. Moreover, the captured image data can include at least one of an image, a video, a sequence of images (i.e., multiple images taken within a time period and/or with an order), a collection of images, or the like, and the term input image is used herein to refer to the various example types of image data.

Presentation device 28 may include, but not limited to, an electronically addressable display, such as a liquid crystal display (LCD) or other type of display device for use with mobile device 28. In some implementations, mobile device 15 generates the content to display on presentation device 28 for the notes in a variety of formats, for example, a list, grouped in rows and/or column, a flow diagram, or the like. Mobile device 15 may, in some cases, communicate display information for presentation by other devices, such as a tablet computer, a projector, an electronic billboard or other external device.

As described herein, mobile device 15, and the software executing thereon, provide a platform for creating and manipulating digital notes representative of physical notes 22. For example, in general, mobile device 15 is configured to process image data produced by image capture device 18 to detect and recognize at least one of physical notes 22 positioned within workspace 20. In some examples, the mobile device 15 is configured to recognize note(s) by determining the general boundary of the note(s). After a note is recognized, mobile device 15 extracts the content of at least one of the one or more notes, where the content is the visual information of note 22.

As further described below, mobile device 15 may implement techniques for automated detection and recognition of physical notes 22 and extraction of information, content or other characteristics associated with each of the physical notes. For example, mobile device 15 may allow user 26 fine grain control over techniques used by mobile device 15 to detect and recognize physical notes 22. As one example, mobile device 15 may allow user 26 to select between marker-based detection techniques in which one or more of notes 22 includes a physical fiducial mark on the surface of the note or non-marker based techniques in which no fiducial mark is used.

In addition, mobile device 15 provides user 26 with an improved electronic environment for generating and manipulating corresponding digital notes representative of physical notes 22. As another example, mobile device 15 may provide mechanisms allowing user 26 to easily add digital notes to and/or delete digital notes from a set of digital notes representative of the brainstorming activity associated with workspace 20. In some example implementations, mobile device 15 provides functionality by which user 26 is able to record and manage relationships between groups of notes 22.

In some example implementations, mobile device 15 provides functionality by which user 26 is able to export the digital notes to other systems, such as cloud-based repositories (e.g., cloud server 12) or other computing devices (e.g., computer system 14 or mobile device 16).

In the example of <figref idref="f0001">FIG. 1</figref>, mobile device 15 is illustrated as a mobile phone. However, in other examples, mobile device 15 may be a tablet computer, a personal digital assistant (PDA), a laptop computer, a media player, an e-book reader, a wearable computing device (e.g., a watch, eyewear, a glove), or any other type of mobile or non-mobile computing device suitable for performing the techniques described herein.

<figref idref="f0002">FIG. 2</figref> illustrates a block diagram illustrating an example of a mobile device that operates in accordance with the techniques described herein. For purposes of example, the mobile device of <figref idref="f0002">FIG. 2</figref> will be described with respect to mobile device 15 of <figref idref="f0001">FIG. 1</figref>

In this example, mobile device 15 includes various hardware components that provide core functionality for operation of the device. For example, mobile device 15 includes one or more programmable processors 70 configured to operate according to executable instructions (i.e., program code), typically stored in a computer-readable medium or data storage 68 such as static, random-access memory (SRAM) device or Flash memory device. I/O 76 may include one or more devices, such as a keyboard, camera button, power button, volume button, home button, back button, menu button, or presentation device 28 as described in <figref idref="f0001">FIG. 1</figref>. Transmitter 72 and receiver 74 provide wireless communication with other devices, such as cloud server 12, computer system 14, or other mobile device 16 as described in <figref idref="f0001">FIG.1</figref>, via a wireless communication interface as described in <figref idref="f0001">FIG. 1</figref>, such as but not limited to high-frequency radio frequency (RF) signals. Mobile device 15 may include additional discrete digital logic or analog circuitry not shown in <figref idref="f0002">FIG. 2</figref>.

In general, operating system 64 executes on processor 70 and provides an operating environment for one or more user applications 77 (commonly referred to "apps"), including note management application 78. User applications 77 may, for example, comprise executable program code stored in computer-readable storage device (e.g., data storage 68) for execution by processor 70. As other examples, user applications 77 may comprise firmware or, in some examples, may be implemented in discrete logic.

In operation, mobile device 15 receives input image data and processes the input image data in accordance with the techniques described herein. For example, image capture device 18 may capture an input image of an environment having a plurality of notes, such as workspace 20 of <figref idref="f0001">FIG. 1</figref> having of notes 22. As another example, mobile device 15 may receive image data from external sources, such as cloud server 15, computer system 14 or mobile device 16, via receiver 74. In general, mobile device 15 stores the image data in data storage 68 for access and processing by note management application 78 and/or other user applications 77.

As shown in <figref idref="f0002">FIG. 2</figref>, user applications 77 may invoke kernel functions of operating system 64 to output a graphical user interface (GUI) 79 for presenting information to a user of mobile device. As further described below, note management application 78 may construct and control GUI 79 to provide an improved electronic environment for generating and manipulating corresponding digital notes representative of physical notes 22. For example, note management application 78 may construct GUI 79 to include a mechanism that allows user 26 to easily add digital notes to and/or deleting digital notes from defined sets of digital notes recognized from the image data. In some example implementations, note management application 78 provides functionality by which user 26 is able to record and manage relationships between groups of the digital notes by way of GUI 79.

<figref idref="f0003">Figure 3</figref> illustrates a block diagram illustrating one example implementation of note management application 78 that operates in accordance with the techniques described herein. Although described as a user application 77 executing on mobile device 15, the examples described herein may be implemented on any computing device, such as cloud server 12, computer system 14, or other mobile devices.

In this example, user application 78 includes image processing engine 82 that provides image processing and object recognition functionality. Image processing engine 82 may include image communication module 90, note identification module 86 and digital note generation module 88. In addition, image processing engine 82 includes image processing APIs 95 that provide a library of image manipulation functions, e.g., image thresholding, masking, filtering, edge detection, and the like, for use by the other components of image processing engine 82.

In general, image data may be stored in storage device 68. In this example, note management application 78 stores images 97 within data storage device 68. Each of images 97 may comprise pixel data for environments having a plurality of physical images, such as workspace 20 of <figref idref="f0001">FIG. 1</figref>.

As described herein, note identification module 86 processes images 97 and identifies (i.e., recognizes) the plurality of physical notes in the images. The input image may be processed by note identification module 86 using marker and/or non-marker detection processes. Digital note generation module 88 generates digital notes 99 corresponding to the physical notes recognized within the image 97. For example, each of digital notes 99 corresponds to one of the physical notes identified in an input image 97. During this process, digital note generation module may update database 94 to include a record of the digital note, and may store within the database information (e.g., content) extracted from the input image within boundaries determined for the physical note as detected by note identification module 86. Moreover, digital note generation module 88 may store within database 94 metadata associating the digital notes into one or more groups of digital notes.

Communication module 90 controls communication of image data between mobile device 15 and external devices, such as cloud server 12, computer system 14, mobile device 16, or image capture device 18. In some examples, communication modules 90 may, for example, allow a user to communicate processed or unprocessed images 97 of environments and/or digital notes and associated information extracted therefrom including metadata from database 68. In some examples, image communication module 90 exports this data to a zip file that may be communicated by FTP, HTTP, email, Bluetooth or other mechanism.

In the example of <figref idref="f0001">FIG. 1</figref>, note management application 78 includes user interface 98 that constructs and controls GUI 79. As described below, user interface 98 may, in some examples, output for display an input image 97 overlaid with the plurality of digital notes 99, where each of the digital notes is overlaid in place of a corresponding physical note. In addition, user interface 98 may display a group of digital notes 99 that has been designated by the user. This group of digital notes 99 may be, for example, a subset of the digital notes recognized in a particular input image 97. User interface 98 may display this designated group (set) of the digital notes on a second portion of GUI 79 and allow user 26 to easily add or remove digital notes 99 from the designated group.

In some example implementations, user interface 98 provides an image editor 96 that allow user 26 to edit the overlay image and/or the digital notes. In another example, digital note generation module 88 may include a process or processes that enhance the extracted information from the input image.

Additional example details of note management application 78 for detecting and recognizing physical notes are described in <patcit id="pcit0001" dnum="US61844140" dnum-type="L"><text>U.S. Patent Application 61/844140, filed July 9</text></patcit>, entitled SYSTEMS AND METHODS FOR NOTE RECOGNITION AND MANAGEMENT USING COLOR CLASSIFICATION," <patcit id="pcit0002" dnum="US61844152" dnum-type="L"><text>U.S. Patent Application 61/844152, filed July 9, 2013</text></patcit>, entitled "SYSTEMS AND METHODS FOR NOTE CONTENT EXTRACTION AND MANAGEMENT USING SEGMENTED NOTES, and <patcit id="pcit0003" dnum="US61844176" dnum-type="L"><text>U.S. Patent Application 61/844176, filed July 9, 2013</text></patcit>, "SYSTEMS AND METHODS FOR NOTE CONTENT EXTRACTION AND MANAGEMENT BY SEGMENTING NOTES."

To better understand the present disclosure, <figref idref="f0004">Figure 4A</figref> illustrates an embodiment of a note recognition system 100A. The system 100A can include a processing unit 110, one or more notes 120, a sensor 130, and note content repository 140. The processing unit 110 can include one or more processors, microprocessors, computers, servers, and other computing devices. The sensor 130, for example, an image sensor, is configured to capture a visual representation of a scene having the one or more notes 120. The sensor 130 can include at least one of a camera, a video recorder, an infrared camera, a CCD (Charge Coupled Device) array, a scanner, or the like. The visual representation can include at least one of an image, a video, a sequence of images (i.e., multiple images taken within a time period and/or with a order), a collection of images, or the like. The processing unit 110 is coupled to the sensor 130 and configured to receive the visual representation. In some cases, the processing unit 110 is electronically coupled to the sensor 130. The processing unit 110 is configured to recognize at least one of the one or more notes 120 from the visual representation. The processing unit 110 is configured to recognize note(s) by determining the general boundary of the note(s). After a note is recognized, the processing unit 110 extracts the content of the note. In some cases, the processing unit 110 is configured to recognize and extract the content of more than one note 120 from a visual representation of a scene having those notes 120.

In some cases, the processing unit 110 can execute software or firmware stored in non-transitory computer-readable medium to implement various processes (e.g., recognize notes, extract notes, etc.) for the system 100A. The note content repository 140 may run on a single computer, a server, a storage device, a cloud server, or the like. In some other cases, the note content repository 140 may run on a series of networked computers, servers, or devices. In some implementations, the note content repository 140 includes tiers of data storage devices including local, regional, and central. The notes 120 can include physical notes arranged orderly or randomly in a collaboration space and the sensor 130 generates a visual representation of the notes 120 in the collaboration space.

In some embodiments, at least some of the one or more notes 120 include a mark, which can facilitate the identification, recognition, and/or authentication of a note. In some embodiments, a mark includes at least one of a barcode, a color block, a color code, a fiduciary mark, a trademark logo, a dot, a hole, and the like. The color, and optionally the shape, of the note itself may be used as the mark to facilitate the identification, recognition, and/or authentication of a note. In some cases, the mark can include a plurality of elements arranged in certain patterns, for example, fiduciary marks at four corners of a rectangular note. In some other cases, the mark can include a plurality of elements, where at least some elements are non-visible elements that can be used to provide authentication information, for example, RFID (radio frequency identification) tags. By way of example, a mark can be made using at least one of a retroreflective material, an optically variable ink, a colored ink, infrared absorbing ink, fluorescent ink, watermark, glossy material, iridescent material, multilayer optical film, colloidal crystals, perforated marks, structured color, floating image, window thread, or the like. In some embodiments, the processing unit 110 first recognizes the mark on a note from a visual representation, determines the location of the mark on the visual representation, and then extracts the content of the note based on the recognized mark. In some cases, the processing unit 110 extracts the content of the note based upon the recognized mark, a known shape of the note, and/or a known relative position of the mark on the note. In some implementations, the processing unit 110 extracts the content of the note from the visual representation in real-time (i.e., process the data in a transitory storage) without storing the visual representation in a non-transitory storage.

In some implementations, the note recognition system <b>100A</b> can include a presentation device (not shown in <figref idref="f0004">Figure 4A</figref>) to show to a user which notes are recognized and/or which notes' content have been extracted. Further, the note recognition system <b>100A</b> can present the extracted content via the presentation device. In some embodiments, not covered by the claimed invention, the processing unit <b>110</b> can authenticate a note before extracting the content of the note. If the note is authenticated, the content will be extracted and stored in the note content repository <b>140.</b> In some cases, the processing unit can extract the authentication information from the mark on the note. In such cases, the authentication information can be visible or non-visible on the mark. For example, the mark on a note can include a symbol, for example, a company logo, a matrix code, a barcode, a color code, or the like. As another example, the mark on the note can include a tag to store information that can be retrieved by a suitable reader. For example, the mark can include a RFID tag, a near field communication (NFC) tag, or the like.

In some embodiments, the sensor <b>130</b> can generate a first visual representation of a scene having several notes <b>120,</b> for example, taking a photograph or a video clip of the notes with the surrounding environment. Each of the notes has a mark. The processing unit <b>110</b> identifies the marks, determines the location of the marks, and uses the location of the marks to control the sensor <b>130</b> to generate a second visual representation of the scene having the notes <b>120,</b> for example, taking a zoomed-in image of the notes. The processing unit <b>110</b> can further recognize and extract content of notes from the second visual representation.

<figref idref="f0004">Figure 4B</figref> illustrates an embodiment, not covered by the claimed invention, of a note management system <b>100B,</b> where the note management system <b>100B</b> includes processing unit <b>110,</b> one or more notes <b>120,</b> one or more note sources <b>150,</b> and a note content repository <b>140.</b> In some cases, the system <b>100B</b> includes a presentation device <b>160.</b> The processing unit <b>110,</b> the notes <b>120,</b> and the note content repository <b>140</b> are similar to the components for the note recognition system <b>100A</b> as illustrated in <figref idref="f0004">Figure 4A</figref>. The note sources <b>150</b> can include sources to provide content of physical notes, such as a visual representation of a scene having one or more notes, and sources to provide content of digital notes, such as a data stream entered from a keyboard. In some embodiments, the note management system <b>100B</b> includes a first source and a second source, and the first source is a visual representation of a scene having one or more notes <b>120.</b> The first source and the second source are produced by different devices. The second source includes at least one of a text stream, an image, a video, a file, and a data entry. The processing unit <b>110</b> recognizes at least one of the notes from the first source and extracts the content of the note, as discussed in the note recognition system <b>100A.</b> In some cases, the processing unit <b>110</b> labels the note with a category. The processing unit <b>110</b> can label a note based on its specific shape, color, content, and/or other information of the note. For example, each group of note can have a different color (e.g., red, green, yellow, etc.). In some cases, a note <b>120</b> can include mark that has one or more elements and the processing unit <b>110</b> can label the note based on information extracted from the mark.

In some embodiments, not covered by the claimed invention, the note management system <b>100B</b> can include one or more presentation devices <b>160</b> to show the content of the notes <b>120</b> to the user. The presentation device <b>160</b> can include, but not limited to, an electronically addressable display, such as a liquid crystal display (LCD), a tablet computer, a projector, an electronic billboard, a cellular phone, a laptop, or the like. In some implementations, the processing unit <b>110</b> generates the content to display on the presentation device <b>160</b> for the notes in a variety of formats, for example, a list, grouped in rows and/or column, a flow diagram, or the like.

Various components of the note recognition system and note management system, such as processing unit, image sensor, and note content repository, can communicate via a communication interface. The communication interface includes, but not limited to, any wired or wireless short-range and long-range communication interfaces. The short-range communication interfaces may be, for example, local area network (LAN), interfaces conforming to a known communications standard, such as Bluetooth standard, IEEE 802 standards (e.g., IEEE 802.11), a ZigBee or similar specification, such as those based on the IEEE 802.15.4 standard, or other public or proprietary wireless protocol. The long-range communication interfaces may be, for example, wide area network (WAN), cellular network interfaces, satellite communication interfaces, etc. The communication interface may be either within a private computer network, such as intranet, or on a public computer network, such as the internet.

<figref idref="f0005">Figures 5A-5C</figref> illustrate some examples of notes having marks. As illustrated in <figref idref="f0005">Figure 5A</figref>, the mark on the note <b>200A</b> has two elements, element <b>210A</b> and element <b>220A.</b> The elements <b>210A</b> and <b>220A</b> can have different size, shape, relative position and/or material composition. For example, element <b>210A</b> is a barcode as an identifier for the note and element <b>220A</b> is a small rectangle of retro-reflective ink that can be used to determine the boundary of the note. As illustrated in <figref idref="f0005">Figure 5B</figref>, the mark on the note <b>200B</b> can have four elements <b>210B, 220B, 230B,</b> and <b>240B.</b> The four elements may have similar or different sizes, shapes, and material compositions. The mark can be used to recognize the size, location, orientation, distortion, and other characteristics of the note, which can be used in content extraction and enhancement. As illustrated in <figref idref="f0005">Figure 5C</figref>, the mark on the note <b>200C</b> has one element <b>210C.</b> The note <b>200C</b> has a non-rectangular shape. In some embodiments, the element <b>210C</b> includes an identifier, for example, to identify the types and the grouping for a note. In some cases, the identifier is a unique identifier.

<figref idref="f0006">Figure 5D</figref> illustrates another example of notes having marks. The marks are the color of the notes. An image <b>200D</b> capturing twelve notes is illustrated in <figref idref="f0006">Figure 5D</figref>. Notes <b>210D, 212D,</b> and <b>214D</b> are magenta. Notes <b>220D, 222D,</b> and <b>224D</b> are yellow. Notes <b>230D, 232D,</b> and <b>234D</b> are blue. Notes <b>240D, 242D,</b> and <b>244D</b> are orange.

<figref idref="f0006">Figures 5E and 5F</figref> illustrate some examples of notes with segments. <figref idref="f0006">Figure 5E</figref> illustrates a note <b>200E</b> with four colored segments. Each segment has a solid color. For example, segment <b>210E</b> is magenta; segment <b>220E</b> is yellow; segment <b>230E</b> is blue; and segment <b>240E</b> is orange. <figref idref="f0006">Figure 5F</figref> illustrates a note <b>200F</b> with four segments and each segment has a colored boundary. For example, segment <b>210F</b> has a magenta boundary; segment <b>220F</b> has a yellow boundary; segment <b>230F</b> has a blue boundary; and segment <b>240F</b> has an orange boundary. In some implementations, the colored boundary has certain line width (e.g., more than 0.1 mm, more than 2% of the overall width of the segment, etc.) such that the boundary can be recognized by an image processing algorithm. In some cases, the color of the segment or the boundary of the segment can be used to categorize the segment. For example, magenta segments or segments with magenta boundary may be associated with a category A and yellow segments or segments with yellow-colored boundary may be associated with a category B. In some other cases, the color of the segment or the boundary of the segment can be used to denote a status or other functional information regard the note. For example, red may denote deletion of the note content; and orange may denote note to be completed.

<figref idref="f0007">Figure 5G</figref> illustrates an example of creating segments of a note using marks. The note <b>200G</b> can be any media with content, for example, a printed document, a white board having writing and/or drawing, a piece of paper with handwritten notes, or the like. The marks can be generated using a pen (i.e., the pen or highlighter <b>260G</b> illustrated in <figref idref="f0007">Figure 5G</figref>), printed marks, flags, stickers, colored tapes, highlighter, colored paper, printed flags with handwritten content, or the like. The note <b>200G</b> has four segments, <b>210G, 220G, 230G,</b> and <b>240G,</b> where each of the segments has a rectangular mark. In some implementations, a segment can be marked at two opposing corners, such as top left corner and bottom right corner. As illustrated in <figref idref="f0007">Figure 5H</figref>, the corner marks can be in the shapes shown as <b>210H</b> and <b>220H.</b> The marks <b>210H</b> and <b>220H</b> are elbow-shaped and symmetric. Marks can include, but not limited to, one or more of lines, arrows, star-shaped marks, elbow-shaped marks, rectangular marks, circular marks, ellipse-shaped marks, polygon-shaped marks, and geometric-shaped marks. In some embodiments, marks used for a corner can be in any geometric shapes, for example, rectangle, circle, star, or the like. In addition, marks can have asymmetric elbow-like shapes.

<figref idref="f0008">Figure 6A</figref> illustrates a flow diagram of an embodiment of a note recognition and/or management system, such as note management application 78 of mobile device 15 (<figref idref="f0001">FIG. 1</figref>) or note recognition system <b>100A</b> (<figref idref="f0004">FIG. 4A</figref>). Initially, the system captures a visual representation of plurality of notes (step <b>310A</b>). The notes are physical notes and it is more efficient to capture more than one note at a time. Next, the system recognizes one or more of the plurality of notes from the visual representation (step <b>320A</b>). For example, the system can recognize a specific mark on a note and subsequently determine the general boundary of the note. The system extracts content of the one or more of the plurality of notes (step <b>330A</b>). At this time, the system may create a respective digital note representative of a corresponding one of the recognized physical notes. The system may associate the content extracted from the visual representation for a given physical note with the corresponding digital note representative of the physical note. In some embodiments, not covered by the claimed invention, the system can apply image transformation to at least part of the visual representation before extracting content. In some other embodiments, not covered by the claimed invention, the system can apply image enhancement or other image processing technique to improve the quality of the extracted content. In yet other embodiments, not covered by the claimed invention, the system can further recognize text and figures from the extracted content.

<figref idref="f0008">Figure 6B</figref> illustrates a flow diagram of an embodiment of extracting content of notes. First, a visual representation is received by the system (step <b>310B</b>). The system recognizes a mark on a note from the visual representation (step <b>320B</b>). After the position and/or shape of the mark is determined, the system may optionally perform geometric correction and cropping to the visual representation (step <b>330B</b>). Based on the recognized position and/or shape of the mark on the note, the general boundary of the note is recognized on the visual presentation (step <b>340B</b>). In some embodiments, the system may receive two visual representations of the same set of notes, where each of the notes has a mark. The first visual representation is taken using a light source suitable to capture the marks on the notes. For example, the light source can be infrared light for marks using infrared sensitive ink. The marks are recognized in the first visual representation and the positions of the marks are determined. The second visual representation can capture the set of notes and the system can determine the general boundary of each note based on its mark respectively. For example, the marks can be made with retro-reflective materials and the first visual representation can be captured with a separate light source (e.g., flash, etc.) and the second visual representation can be captured without a separate light source. After the general boundary of a note is determined, the system extracts the content of the note (step 350B). At this time, the system may create a respective digital note representative of a corresponding one of the recognized physical notes. The system may associate the content extracted from the visual representation for a given physical note with the corresponding digital note representative of the physical note. As another example, where the visual representation is an image, the system may crop the image according to the determined general boundary of the note. Optionally, the system may enhance the content of the note (step 360B), for example, by changing the contrast, brightness, and/or using other image processing techniques (e.g., white balancing, contrast stretching, etc.). In some cases, the system may update the note with a status indicating its content extracted.

<figref idref="f0009">Figure 6C</figref> illustrates a flow diagram of an embodiment, not covered by the claimed invention, of authenticating a note. First, obtain an authentication signal from a note or a stack of notes (step 310C). In one embodiment, not covered by the claimed invention, the authentication information is a visual component (e.g., a hologram) of a mark on a note and the authentication signal can be obtained by extracting the visual component from a visual representation capturing the note. In another embodiment, not covered by the claimed invention, the authentication information is contained in an electronic component (e.g., a RFID tag) of a mark on a note and the authentication signal can be obtained using a suitable reader (e.g., a RFID reader). Next, the system verifies the authentication signal (step 320C). The system can start the note management application or use the note(s) if the note(s) are authenticated (step 330C). In some cases, authentication can be done before capturing the notes.

<figref idref="f0010">Figure 6D</figref> illustrates a functional flow diagram of an embodiment of extracting content of notes or note segments using color classification algorithms. The method includes training steps for computing classification function(s), also referred to as classifier, (step 350D-365D). Initially, image data of capturing a visual representation of note(s) or a note with note segment(s) (step 310D) within or without the surrounding environment is received. In some cases, an image is retrieved from the visual representation (image data) (step 315D) if the visual representation is not a single image. For example, the visual representation is a set of images, and the image retrieved is an aggregation of at least part of the set of images. As another example, the visual representation is a video, and the image retrieved is a combination of several or all frames of the video. The image is converted to a desirable color space (step 317D), which is LAB (e.g., Hunter 1948 L,a,b color space, CIE 1976 (L^∗, a^∗, b^∗) color space); other color spaces that can be used in addition are RGB, CMYK(cyan, magenta, yellow, and key (black)), HSV (hue, saturation, and value), HSL (hue, saturation, and lightness), HSI (hue, saturation, and intensity), sRGB (standard red, green, and blue) color space. Next, apply one or more classification functions to color values for each pixel or group of pixels in the image (step 320D). The classification functions are computed using the training steps 350D-365D. The classification algorithm is linear discriminant analysis and other algorithms that can additionally be used include quadratic classifier, Gaussian Mixture Models, Boosted Decision Trees, Support Vector Machines or the like.

Some classifiers are generative in nature while others are discriminative. In general, generative classifiers generate an individual model for each class (in our case a color) and a queried pixel / group of pixels value is given a probability score as to whether it belongs to that class or not. Discriminative classifiers on the other hand model the boundary between two or more classes (2-class and multiclass classification respectively). Generative models provide easier generalizability to new classes (colors) that are not already modeled while separate discriminative models have to be retrained to every existing class (colors). Many examples of generative and discriminative classifiers are described in <nplcit id="ncit0007" npl-type="b"><text>Christopher M. Bishop, 2006, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, USA</text></nplcit>. Some examples of generative models are naive Bayes classifier, Gaussian Mixture Models and other probabilistic Bayesian models using Gaussian or other priors, and Dictionary Learning, such as those described in <nplcit id="ncit0008" npl-type="s"><text>Michal Aharon, Michael Elad, and Alfred Bruckstein (2006), "K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation", IEEE Transactions on Signal Processing 54 (11): 4311-4322</text></nplcit>). Some examples of discriminative classification models are neural networks, Support Vector Machines, Decision Trees, Linear and Quadratic discriminate classification, logistic regression. Other example classification methods are neither generative nor discriminative e.g. nearest neighbor classification algorithm. In addition, the performance of any of the above mentioned classification models can be improved by ensemble learning algorithms that combine multiple instances of classification models mentioned above. Ensemble learning may be implemented as Bagging, Boosting, and their variants.

Using the classification algorithms, indicators indicative of color classes for each pixel or group of pixels is generated in the image based on color values of the pixel or group of pixels (step 325D). A color class includes a particular range of wavelength or can be an "other" color class referring to any other color besides the color classes of interest. For example, a color class can be magenta, yellow, blue, orange, etc. An indicator can be represented by, for example, a number, a code, a symbol, an alphanumerical token, a color value, a grayscale value, or the like.

Based on the indicators, the general boundary of note(s) or note segment(s) is determined (step <b>330D</b>). In one embodiment, the adjacent pixels with a same indicator are grouped into a region and the boundary of the region can be determined. In some cases, the indicators are further processed using image processing principles, for example, noise filtering, morphological opening, or the like, before the general boundary is determined. The shape and/or the size of a note or note segment is predetermined, which is used to determine and/or filter the general boundary of note(s) or note segment(s). At this time, the system may create a respective digital note representative for each of the physical notes for which a boundary is determined.

Using the general boundaries, the content of the note(s) or note segment(s) can be extracted (step <b>335D</b>). The system may associate the content extracted from the visual representation for a given physical note with the corresponding digital note representative of the physical note. In some embodiments, the note(s) or note segment(s) may have colored boundaries or colored mark(s) at one or more locations instead of having solid color. In such embodiments, a same color classification algorithm can be used to identify the boundaries or marks and further extract the content of the note(s) or note segment(s). In some cases, a set of content, where each piece of content corresponds to a note or a note segment, can be presented to user via a graphical interface or in a report. In some implementations, the set of content can be grouped with categories, where each category is associated with a color, a shape, a size, or a combination thereof used in the note. For example, notes <b>210D, 212D</b> and <b>214D</b> illustrated in <figref idref="f0006">Figure 5D</figref> are associated with a category A and notes <b>220D, 222D,</b> and <b>224D</b> are associated with a category B. As another example, a note segment with boundary of magenta (e.g., note segment <b>210F</b> illustrated in <figref idref="f0006">Figure 5F</figref>) is associated with category A and a note segment with boundary of yellow (e.g., note segment <b>220F</b> illustrated in <figref idref="f0006">Figure 5F</figref>) is associated with category B.

In some cases, the generated indicators only indicate whether the pixel's or groups of pixels' color value is a particular color class. For example, an indicator of 1 indicates the pixel or group of pixels is yellow and an indicator of 0 indicates the pixel is not yellow. In such cases, a set of indicators will be generated for each color class of interest. In some embodiment, steps <b>320D-335D</b> are repeated for each color class of interest, for example, magenta, yellow, blue, and orange.

The classification functions are determined using the steps illustrated in <figref idref="f0010">Figure 6D</figref>. First, a set of training images are provided (step 350D). In some cases, the training images can include notes or note segments with the same color classes of interest. The training images can be captured under one or more lighting conditions and/or one or more different cameras. For example, the training images can be a set of six images captured in three lighting conditions using two different cameras. The training images are optionally converted to a desired color space (step 355D). Train the classifier (step 360D) using the known color for corresponding color values of a pixel or group of pixels, for example using equations (1)-(4) as discussed below. The set of classification function(s) for color class is then computed (step 365D). In some cases, a set of classification function(s) for only one color class is obtained using steps 350D-365D. These steps are repeated to obtain classification functions for each of other color classes of interest.

In some embodiments, a color class can include two or more similar colors. The classification function for such a color class can be obtained using the same process as discussed above. In some cases, a hierarchical approach may be used for extracting notes or note segments having more than two colors, as illustrated in <figref idref="f0011">Figure 6E. Figure 6E</figref> illustrates a functional flow diagram of an example of extracting content of notes or note segments having three different colors (yellow, orange, and blue) using color classification algorithm. Initially, the system uses a color classification algorithm to determine whether a pixel has a color that belongs to a color class including yellow and orange (step 310E). If the color is yellow or orange, the system further evaluates whether the color is yellow (step 320E): if it is yellow, an indicator for yellow can be associated with the pixel or group of pixels (step 340E); and if it is not yellow, an indicator for orange can be associated with the pixel or group of pixels (step 350E). If the color does not belong to the color class including yellow and orange, the system evaluates whether the color is blue (step 330E) using the color classification algorithm: if the color is blue, an indicator for blue can be associated with the pixel or group of pixels (step 360E); and if the color is not blue, an indicator of other color can be associated with the pixel or group of pixels (step 370E).

An image of captured notes or note segments is converted into LAB color space. The LAB color space separates different colors on the ab plane, and it is possible to differentiate different color in this color space using a threshold approach. Table 1 lists the pseudo code to an example of extracting notes or note segments using a threshold approach. <tables id="tabl0001" num="0001"><table frame="all"><title>Table 1. Pseudo Code for Simple Color "b" Threshold</title><tgroup cols="2" colsep="0" rowsep="0"><colspec colnum="1" colname="col1" colwidth="41mm" /><colspec colnum="2" colname="col2" colwidth="41mm" colsep="1" /><tbody><row><entry namest="col1" nameend="col2" align="left">Load an image (from a file or using other methods)</entry></row><row><entry namest="col1" nameend="col2" align="left">Convert to Lab color space</entry></row><row><entry namest="col1" nameend="col2" align="left">If (b value &gt; 75% maximum b value)</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 255</entry></row><row><entry namest="col1" nameend="col2" align="left">else</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 0</entry></row><row><entry namest="col1" nameend="col2" align="left">Morphological erode grayscaleResult 2 passes</entry></row><row><entry namest="col1" nameend="col2" align="left">Median filter grayscaleResult</entry></row><row><entry namest="col1" nameend="col2" align="left">Morphological dilate grayscaleResult 2 passes</entry></row><row><entry namest="col1" nameend="col2" align="left">Find contours of all regions with same grayscaleResult</entry></row><row><entry namest="col1" nameend="col2" align="left">Eliminate small regions (less than 1% of total image size)</entry></row><row rowsep="1"><entry namest="col1" nameend="col2" align="left">Extract notes or note segments</entry></row></tbody></tgroup></table></tables>

While the above pseudocode and description explains a two class linear discriminate classification process, there are multiclass extensions of the same, such as those described in <nplcit id="ncit0009" npl-type="b"><text>Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA</text></nplcit>.

In some cases, the threshold approach may not be adaptive to various capture conditions, such as variation of colors, lighting, camera type, ambience, etc. To address this issue, adaptive models can be used, which can learn the best representations for different colors that are robust to various capture conditions and additional colored notes. One such model is the Fischer's Linear Discriminant model or Fischer's Linear Discriminant Classifier, as described by<nplcit id="ncit0010" npl-type="b"><text> Mika, S.; Ratsch, G.; Weston, J.; Scholkopf, B.; Muller, K., Fisher discriminant analysis with kernels, Neural Networks for Signal Processing IX, 1999, Proceedings of the 1999 IEEE Signal Processing Society Workshop., pages 41-48, Aug 1999</text></nplcit>.

Given a set of m observations X (i.e., the training images), where each observation is 3 dimensional in this case (L, a and b values for the LAB color space) and the corresponding color class for all observations <b>Y,</b> a weight vector <b>w</b> is to be determined such that <maths id="math0001" num="(1)"><math display="block"><mi mathvariant="bold">w</mi><mo>.</mo><mi mathvariant="bold">X</mi><mo>+</mo><mi mathvariant="normal">c</mi><mo>&lt;</mo><mi mathvariant="normal">0</mi><mspace width="1ex" /><mi>if</mi><mspace width="1ex" /><mi mathvariant="bold">Y</mi><mo>=</mo><mi mathvariant="normal">1</mi><mfenced separators=""><mi>belongs</mi><mspace width="1ex" /><mi>to</mi><mspace width="1ex" /><mi>the</mi><mspace width="1ex" /><mi>color</mi><mspace width="1ex" /><mi>class</mi><mspace width="1ex" /><mi>of</mi><mspace width="1ex" /><mi>interest</mi></mfenced><mo>,</mo><mi>and</mi></math><img file="EP3020002B1_D0001.tif" /></maths><maths id="math0002" num="(2)"><math display="block"><mi mathvariant="bold">w</mi><mo>.</mo><mi mathvariant="bold">X</mi><mo>+</mo><mi mathvariant="normal">c</mi><mo>&gt;</mo><mi mathvariant="normal">0</mi><mspace width="1ex" /><mi>if</mi><mspace width="1ex" /><mi mathvariant="bold">Y</mi><mo>=</mo><mn>0</mn><mfenced separators=""><mi>does</mi><mspace width="1ex" /><mi>not</mi><mspace width="1ex" /><mi>belong</mi><mspace width="1ex" /><mi>to</mi><mspace width="1ex" /><mi>the</mi><mspace width="1ex" /><mi>color</mi><mspace width="1ex" /><mi>class</mi><mspace width="1ex" /><mi>of</mi><mspace width="1ex" /><mi>interest</mi></mfenced><mo>,</mo></math><img file="EP3020002B1_D0002.tif" /></maths> where <b>w</b> . <b>X</b> is a dot product operation and c are off-set values. The weights <b>w</b> depends on the mean values of L, a, and b of each color class and the covariance matrix of each color class. Specifically, the weight <b>w</b> maximizes <b>S,</b> where <maths id="math0003" num="(3)"><math display="block"><mi mathvariant="bold">S</mi><mo>=</mo><msub><msup><mi>σ</mi><mn>2</mn></msup><mrow><mi mathvariant="bold">between</mi><mo>−</mo><mi mathvariant="bold">class</mi></mrow></msub><mo>/</mo><msub><msup><mi>σ</mi><mn>2</mn></msup><mrow><mi mathvariant="bold">within</mi><mo>−</mo><mi mathvariant="bold">class</mi></mrow></msub></math><img file="EP3020002B1_D0003.tif" /></maths> i.e., <b>w</b> is learned such that the between-class variance is maximized and within-class variance is minimized.

This results in a <maths id="math0004" num="(4)"><math display="block"><mi mathvariant="bold">w</mi><mo>=</mo><msup><mfenced separators=""><msub><mi mathvariant="normal">Σ</mi><mrow><mi mathvariant="normal">y</mi><mo>=</mo><mn>0</mn></mrow></msub><mo>+</mo><msub><mi mathvariant="normal">Σ</mi><mrow><mi mathvariant="normal">y</mi><mo>=</mo><mn>1</mn></mrow></msub></mfenced><mrow><mo>−</mo><mn>1</mn></mrow></msup><mfenced separators=""><msub><mi mathvariant="normal">μ</mi><mrow><mi mathvariant="normal">y</mi><mo>=</mo><mn>1</mn></mrow></msub><mo>−</mo><msub><mi mathvariant="normal">μ</mi><mrow><mi mathvariant="normal">y</mi><mo>=</mo><mn>0</mn></mrow></msub></mfenced></math><img file="EP3020002B1_D0004.tif" /></maths>

The class mean and covariance (µ and ∑) can be estimated from the training image. If it is assumed that the class covariance are identical then the model is linear. If this assumption is not made, then the model is a quadratic discriminant classifier. The classification method for the quadratic discriminant classifier has an additional term: <maths id="math0005" num="(5)"><math display="block"><msup><mi mathvariant="bold">X</mi><mi>T</mi></msup><mi mathvariant="italic">QX</mi><mo>+</mo><mi mathvariant="bold">wX</mi><mo>+</mo><mi mathvariant="bold">c</mi><mo>&lt;</mo><mn>0</mn><mspace width="1ex" /><mi mathvariant="bold">if</mi><mspace width="1ex" /><mi mathvariant="bold">Y</mi><mo>=</mo><mn>1</mn><mfenced separators=""><mi>belong</mi><mspace width="1ex" /><mi>to</mi><mspace width="1ex" /><mi>class</mi><mspace width="1ex" /><mi>in</mi><mspace width="1ex" /><mi>question</mi></mfenced></math><img file="EP3020002B1_D0005.tif" /></maths><maths id="math0006" num="(6)"><math display="block"><msup><mi mathvariant="bold">X</mi><mi>T</mi></msup><mi mathvariant="italic">QX</mi><mo>+</mo><mi mathvariant="bold">wX</mi><mo>+</mo><mi mathvariant="bold">c</mi><mo>&gt;</mo><mo>=</mo><mn>0</mn><mspace width="1ex" /><mi mathvariant="bold">if</mi><mspace width="1ex" /><mi mathvariant="bold">Y</mi><mo>=</mo><mn>0</mn><mfenced separators=""><mi>does</mi><mspace width="1ex" /><mi>not</mi><mspace width="1ex" /><mi>belong</mi><mspace width="1ex" /><mi>to</mi><mspace width="1ex" /><mi>class</mi><mspace width="1ex" /><mi>in</mi><mspace width="1ex" /><mi>question</mi></mfenced></math><img file="EP3020002B1_D0006.tif" /></maths>

For example, after the linear discriminant model is applied to an image having magenta notes and other background color, the resulting projections are scalar values and the distributions of the scalar projection values are shown in <figref idref="f0021">Figure 14</figref>. The classifier can be further trained to recognize color in images captured in various conditions using the same process and equations. As an example example, for a set of coefficients [w1 w2 w3 c] where w1, w2, and w3 are the coefficients corresponding to L, a, and b respectively and c is the offset, the coefficients for color class blue is [w1 = 0.05056 w2 = 0.34929 w3 = 0.4381 c = -106.71]; and the coefficients for color class yellow is [w1 = 0.0068796 w2 = 0.027731 w3 = -0.14887 c = 18.812].

Table 2 lists the pseudo code for an embodiment of using linear discriminant classifier and some examples of filtering steps. Here, Lcoefficent, Acoefficient, Bcoefficient and offset correspond to w1, w2, w3 and c in the above example. The example of filtering assumes the notes are rectangular. Other color spaces such as RGB and HSV can follow similar approaches, and the pseudo code is listed in Table 3 and Table 4. <tables id="tabl0002" num="0002"><table frame="all"><title>Table 2. Pseudo Code for Linear Discriminant Classifier (LDC) in LAB color space</title><tgroup cols="2" colsep="0" rowsep="0"><colspec colnum="1" colname="col1" colwidth="122mm" /><colspec colnum="2" colname="col2" colwidth="44mm" colsep="1" /><tbody><row><entry namest="col1" nameend="col2" align="left">Load RGB image (from file or other method)</entry></row><row><entry namest="col1" nameend="col2" align="left">Convert to LAB color space</entry></row><row><entry namest="col1" nameend="col2" align="left">LDC = L ^∗ Lcoefficient + A ^∗ Acoefficient + B ^∗ Bcoefficient + offset</entry></row><row><entry namest="col1" nameend="col2" align="left">If (LDC &lt; 0.0)</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 255</entry></row><row><entry namest="col1" nameend="col2" align="left">else</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 0</entry></row><row><entry>Morphological erode grayscaleResult 5 passes</entry>&lt;entry /&gt;</row><row><entry>Median filter grayscaleResult</entry>&lt;entry /&gt;</row><row><entry>Morphological dilate grayscaleResult 5 passes</entry>&lt;entry /&gt;</row><row><entry>Find contours of all regions</entry>&lt;entry /&gt;</row><row><entry>Delete region contours for regions with an area less than a specified percent of image area</entry>&lt;entry /&gt;</row><row><entry>Find best fit rotated rectangle of each remaining region</entry>&lt;entry /&gt;</row><row><entry>Delete region contours for regions with an area of less than 0.8 times the area of the best fit rotated rectangles found in the previous step</entry>&lt;entry /&gt;</row><row rowsep="1"><entry>Extract notes or note segments</entry>&lt;entry /&gt;</row></tbody></tgroup></table></tables>

A morphological opening process can be used to reduce noise, which includes morphological erosion followed by dilation. In some cases, the morphological erosion and dilation can use a structuring element which is disk shaped or circular. For example, the size of the structuring element is set at 5 by 5 for an image with a maximum dimension of 800 pixels. If the image is larger, the size of the structural element can be bigger. This type of noise reduction approach are described in<nplcit id="ncit0011" npl-type="s"><text> Image Analysis and Mathematical Morphology by Jean Serra, ISBN 0-12-637240-3 (1982</text></nplcit>);<nplcit id="ncit0012" npl-type="s"><text> Image Analysis and Mathematical Morphology, Volume 2: Theoretical Advances by Jean Serra, ISBN 0-12-637241-1 (1988</text></nplcit>); and <nplcit id="ncit0013" npl-type="s"><text>An Introduction to Morphological Image Processing by Edward R. Dougherty, ISBN 0-8194-0845-X (1992</text></nplcit>). <tables id="tabl0003" num="0003"><table frame="all"><title>Table 3. Pseudo Code for Linear Discriminant Classifier (LDC) in RGB color space</title><tgroup cols="2" colsep="0" rowsep="0"><colspec colnum="1" colname="col1" colwidth="62mm" /><colspec colnum="2" colname="col2" colwidth="65mm" colsep="1" /><tbody><row><entry namest="col1" nameend="col2" align="left">Load RGB image (from file or other method)</entry></row><row><entry namest="col1" nameend="col2" align="left">LDC = R ^∗ Rcoefficient + G ^∗ Gcoefficient + B ^∗ Bcoefficient + offset</entry></row><row><entry namest="col1" nameend="col2" align="left">If (LDC &lt; 0.0)</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 255</entry></row><row><entry namest="col1" nameend="col2" align="left">else</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 0</entry></row><row><entry namest="col1" nameend="col2" align="left">Morphological erode grayscaleResult 5 passes</entry></row><row><entry namest="col1" nameend="col2" align="left">Median filter grayscaleResult</entry></row><row><entry namest="col1" nameend="col2" align="left">Morphological dilate grayscaleResult 5 passes</entry></row><row><entry namest="col1" nameend="col2" align="left">Find contours of all regions</entry></row><row><entry namest="col1" nameend="col2" align="left">Delete region contours for regions with an area less than a specified percent of image area</entry></row><row rowsep="1"><entry namest="col1" nameend="col2" align="left">Extract notes or note segments</entry></row></tbody></tgroup></table></tables><tables id="tabl0004" num="0004"><table frame="all"><title>Table 4. Pseudo Code for Linear Discriminant Classifier (LDC) in HSV color space</title><tgroup cols="2" colsep="0" rowsep="0"><colspec colnum="1" colname="col1" colwidth="62mm" /><colspec colnum="2" colname="col2" colwidth="65mm" colsep="1" /><tbody><row><entry namest="col1" nameend="col2" align="left">Load RGB image (from file or other method)</entry></row><row><entry namest="col1" nameend="col2" align="left">Convert to HSV color space</entry></row><row><entry namest="col1" nameend="col2" align="left">LDC = H ^∗ Hcoefficient + S ^∗ Scoefficient + V ^∗ Vcoefficient + offset</entry></row><row><entry namest="col1" nameend="col2" align="left">If (LDC &lt; 0.0)</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 255</entry></row><row><entry namest="col1" nameend="col2" align="left">else</entry></row><row>&lt;entry /&gt;<entry>grayscaleResult = 0</entry></row><row><entry namest="col1" nameend="col2" align="left">Morphological erode grayscaleResult 5 passes</entry></row><row><entry namest="col1" nameend="col2" align="left">Median filter grayscaleResult</entry></row><row><entry namest="col1" nameend="col2" align="left">Morphological dilate grayscaleResult 5 passes</entry></row><row><entry namest="col1" nameend="col2" align="left">Find contours of all regions</entry></row><row><entry namest="col1" nameend="col2" align="left">Delete region contours for regions with an area less than a specified percent of image area</entry></row><row rowsep="1"><entry namest="col1" nameend="col2" align="left">Extract notes or note segments</entry></row></tbody></tgroup></table></tables>

<figref idref="f0012">Figure 6F</figref> illustrates a functional flow diagram of an embodiment of extracting a set of content from a note with segments. First, receive a visual representation of a note having color segments (step <b>310F</b>). That is, image data capturing the visual representation may be received. In some cases, an image from the visual representation (step <b>320F</b>) may be retrieved, e.g., if the image data comprises video or a collection of a plurality of images. If the visual representation is an image, the image will be used for further processing. Next, indicators indicative of color classes of respective pixels or group of pixels are generated in the image (step <b>330F</b>), using, for example, a color classification algorithm. Using the indicators, general boundaries of the color segments are determined (step <b>340F</b>). Extract a set of content from the note segments (step <b>350F</b>). In some cases, each piece of content is extracted from a corresponding note segment. Optionally, present a list of content to a user (step <b>360F</b>). In some cases, a piece of content can be associated with a category. A note management system may use the color in the color segment to select the category for the piece of content. At this time, the system may create a respective digital note representative of a corresponding one of the recognized physical notes. As one example, the record may be created to contain a plurality of database fields, including fields for storing the particular content of each color segment of the physical note. The system may associate the content extracted from the visual representation for a given physical note with the corresponding digital note representative of the physical note. Moreover, the system may associate the content piece extracted from each color segment with the digital representation of each color segment. For example, the system may update the fields for the respective record within the database to store respective ones of the content pieces for the different color segments.

<figref idref="f0013">Figure 6G</figref> illustrations a functional flow diagram of an embodiment of segmenting a note and extracting a set of content for the segments. First, a user may segment a note using marks (step <b>310G</b>). The marks can be applied to one or more corners, along at least a part of the boundary of the segment. A note management system can capture an image of the note (step <b>320G</b>). The system further identifies the marks in the image (step <b>330G</b>). The general boundaries of the segments can be determined (step <b>340G</b>). In some cases, some additional information, such as the relative positions of the marks, can be used to determine the general boundaries. Extract a set of content from the note segments using the determined boundaries (step <b>350G</b>). In some cases, each piece of content is extracted from a corresponding note segment. Optionally, present a list of content to a user (step <b>360G</b>). At this time, the system may create a respective digital note representative of a corresponding one of the recognized physical notes. As one example, the record may be created to contain a plurality of database fields, including fields for storing the particular content of each color segment of the physical note. The system may associate the content extracted from the visual representation for a given physical note with the corresponding digital note representative of the physical note. Moreover, the system may associate the content piece extracted from each color segment with the digital representation of each color segment. For example, the system may update the fields for the respective record within the database to store respective ones of the content pieces for the different color segments.

<figref idref="f0014">Figures 7A - 7D</figref> illustrate an example of content extraction process of a note with a mark. First, as illustrated in <figref idref="f0014">Figure 7A</figref>, a visual representation <b>400</b> of a note <b>410</b> is captured. The note <b>410</b> has a mark <b>420,</b> which can be a barcode, a color code, a matrix code, a color block, or the like. Next, as illustrated in <figref idref="f0014">Figure 7B</figref>, the system determines the general boundary <b>430</b> of the mark on the visual representation and recognizes the mark <b>420.</b> In some cases, the note <b>410</b> may be slanted in the visual representation <b>400,</b> as illustrated in <figref idref="f0014">Figure 7C</figref>. In some other cases, the visual representation <b>400</b> may be taken with geometric distortion. The system may use the determined general boundary of the mark <b>420</b> or a portion of the mark <b>420</b> to determine the necessary image transformation and correction to the visual representation <b>400</b> to obtain the note content. <figref idref="f0014">Figure 7D</figref> illustrates that the system extracts the content of the note <b>410</b> after the previous analysis and/or image processing.

<figref idref="f0014 f0015">Figures 8A - 8D</figref> illustrate an embodiment of content extraction of a plurality of notes with retroreflective marks. A note recognition/management system receives a visual representation <b>500A,</b> which captures three notes <b>510A,</b> and each note <b>510A</b> has a mark <b>520</b> having two elements - two retroreflective rectangular tags at upper left and lower bottom corners. Because of the optical property of the retroreflective material, the mark <b>520</b> is substantially brighter than the rest of the note. <figref idref="f0015">Figure 8B</figref> illustrates the marks <b>520</b> are much brighter than the notes <b>510A</b> and the background. In some embodiments, the system may use image processing to transform the visual representation of <b>500A</b> as illustrated in <figref idref="f0014">Figure 8A</figref> to <b>500B</b> as illustrated in <figref idref="f0015">Figure 8B</figref>. In some alternative embodiments, the system may generate another visual representation <b>500B</b> of the three notes <b>510A</b> with a different light source, for example, a flashlight. The system can easily identify the marks <b>520</b> from the visual representation <b>500B.</b> After the marks <b>520</b> are identified, the system can extract the content of the notes <b>510C,</b> as illustrated in <figref idref="f0015">Figure 8C</figref>. In some cases, the system may use image processing techniques (e.g., adaptive histogram equalization) to enhance the extracted content <b>510D,</b> as illustrated in <figref idref="f0015">Figure 8D</figref>.

<figref idref="f0016">Figure 9</figref> illustrates a module diagram of an embodiment of a note recognition system <b>600.</b> In the embodiment as illustrated, the system <b>600</b> includes a note capturing module <b>620,</b> a note recognition module <b>630,</b> and a note extraction module <b>640.</b> Various components of the note recognition system <b>600</b> can be implemented by one or more computing devices, including but not limited to, circuits, a computer, a processor, a processing unit, a microprocessor, and/or a tablet computer. In some cases, various components of the note recognition system <b>600</b> can be implemented on a shared computing device. Alternatively, a component of the system <b>600</b> can be implemented on multiple computing devices. In some implementations, various modules and components of the system <b>600</b> can be implemented as software, hardware, firmware, or a combination thereof. In some cases, various components of the note recognition system <b>600</b> can be implemented in software or firmware executed by a computing device. Additionally, various components of the system <b>600</b> can communicate via or be coupled to via a communication interface, for example, a wired or wireless interface. The note capturing module <b>620</b> is configured to capture a visual representation of a plurality of notes. In some embodiments, the note capturing module <b>620</b> includes an image sensor. The note recognition module <b>630</b> is coupled to the note capturing module <b>620,</b> the note recognition module is configured to receive the captured visual representation and determine a general boundary of one of the plurality of notes from the captured visual representation. The note extraction module <b>640</b> is configured to extract content of the one of the plurality of notes from the captured visual representation based on the determined general boundary of the one of the plurality of notes.

In some embodiments, not covered by the claimed invention, the note recognition system 600 includes a note authentication module 650 configured to authenticate the plurality of notes. In some cases, at least some of the plurality of notes have a mark. The mark can be used to facilitate the recognition, extraction and authentication of the note. For example, the note authentication module 650 can obtain an authentication signal from a mark of a note and verify the authentication based on the authentication signal. In some cases, the note authentication module 650 is coupled to the note recognition module 630 and provides the authentication information to the note recognition module 630. In some embodiments, the note recognition module 630 and the note extraction module 640 can perform the steps illustrated in <figref idref="f0008">Figure 6B</figref> and in the relevant discussions.

In some embodiments, a note recognition/management system may use multiple recognition algorithms to recognize notes and extract notes' content, including color recognition, and, optionally, shape recognition and/or pattern recognition. For example, the system uses a LAB color space and, optionally, color spaces such as the RGB, HSV, CIELAB, etc. to identify regions of interest corresponding to the notes for color recognition. In some cases, the notes are further distinguished in their shape and due to the presence of unique patterns detected by shape recognition (e.g., Hough transform, shape context, etc.) and pattern recognition algorithms (e.g., Support Vector Machine, cross-correlation, template matching, etc.) respectively. These algorithms help filter out unwanted objects in the visual representation or other sources of notes' content and leave only those regions of interest corresponding to the notes.

In some embodiments, a note may include a mark made using fluorescent materials, such as printable inks or coatable dyes. For example, a fiducial mark such as a logo can be printed in fluorescent ink on the writing surface of a note. An appropriate light source would excite the fluorescent material. For example, a white LED (light emitting diode) of a mobile handheld device may be able to excite the fluorophore using the significant blue wavelength component of the LED output spectrum. In one embodiment, a fluorescent dye can be coated on the writing surface of the notes or included in the materials making the notes. In this embodiment, the fluorescent dye can provide not only verification of a branded product, but can also improve the contrast between the written information on the note and the background of the note itself. Such a mark can facilitate the recognition and segmentation of notes on a visual representation produced by an image sensor. In the cases of notes made from paper with fluorescent dye, the captured visual representation may have better readability. Depending on the excitation wavelength range and the fluorescing wavelength range for the fluorescent materials, additional optical equipment, such as a filter, may be used together with an image sensor (e.g., a camera) to improve detection.

<figref idref="f0016">Figure 10A</figref> illustrates a flow diagram of an embodiment of a note management system. First, the system receives a plurality of notes from multiple sources (step 710A). For example, the note management system may receive a set of images of a number of notes from a camera or a smart phone and receive another set of images of a number of notes taken from a remote location. As another example, the note management system may receive a visual representation (e.g., a video) of a number of notes taken by a video recording device and a text stream of notes that is entered via a laptop. In some embodiments, the multiple sources are sources of notes' content taken from different devices, for example, cameras, scanners, computers, etc. Then, the system recognizes one of the plurality of notes (step <b>720A</b>). The system extracts content of the plurality of notes (step <b>730A</b>). In some embodiments, some notes include marks (e.g., color block, color code, barcode, etc.) on the note and one source of notes is a visual representation of some of the notes. In some cases, the recognizing step includes recognizing marks on the notes from the visual representation and then determines the general boundaries of the notes based on the recognized marks. In some of these cases, the extracting step includes extracting the content based upon the recognized marks, known shapes of the notes, and known relative positions of the marks on the notes. After the content of the plurality of notes is extracted, in some cases, the system may associate each of the plurality of notes with a unique identifier (step <b>740A</b>). The system may label each of the plurality of notes with a category (step <b>750A</b>). The labeling step is discussed in more details below. Additionally, the system may first authenticate the notes before recognizing the notes. Optionally, the system may present the content of the plurality of notes on a display (step <b>760A</b>). In some embodiments, not covered by the claimed invention, the system may present the extracted content of the plurality of notes with an output field indicating the categories of the notes. In some implementations, the system may use the category information of the notes in presenting the notes (e.g., show the notes in one category in a group).

<figref idref="f0017">Figure 10B</figref> illustrates examples of how a system may label a note. In one embodiment, not covered by the claimed invention, a note management system can label a note based on a detected signal (<b>710B</b>). For example, a note can have a mark including a barcode; the system can read the barcode and label the note based on the barcode. In some cases, the system can label a note based on a recognized mark (<b>720B</b>). For example, the mark can include an icon, logo, a colored block, or other graphical symbol indicating a particular group. The system may further label a note based on its content (<b>730B</b>). In some cases, the system may label a noted based on historical data and/or predictive modeling (<b>740B</b>). In some other cases, the system may label a note by user input (<b>750B</b>). A note management system can use one or more approaches to label a note. The system may also use other approaches to label a note that are not listed in <figref idref="f0017">Figure 10B</figref>, for example, label a note based on the shape of the note. Further, in some cases, a note may be associated with more than one category.

<figref idref="f0017">Figure 11</figref> illustrates a module diagram of a note management system <b>800.</b> In the embodiment as illustrated, the system <b>800</b> includes one or more note sources <b>820,</b> a note recognition module <b>830,</b> a note extraction module <b>840,</b> and a note labeling module <b>860.</b> Various components of the note management system <b>800</b> can be implemented by one or more computing devices, including but not limited to, circuits, a computer, a processor, a processing unit, a microprocessor, and/or a tablet computer. In some cases, various components of the note management system <b>800</b> can be implemented on a shared computing device. Alternatively, a component of the system <b>800</b> can be implemented on multiple computing devices. In some implementations, various modules and components of the system <b>800</b> can be implemented as software, hardware, firmware, or a combination thereof. In some cases, various components of the note management system <b>800</b> can be implemented in software or firmware executed by a computing device. Additionally, various components of the system <b>800</b> can communicate via or be coupled to via a communication interface, for example, a wired or wireless interface. The note source(s) <b>820</b> is configured to provide a plurality of notes. In some embodiments, one of the note sources <b>820</b> is a visual representation of a scene having one or more notes. In some cases, the note sources <b>820</b> include a plurality of different sources for providing notes, for example, images, text stream, video stream, or the like. The note recognition module <b>830</b> is coupled to the note sources <b>820,</b> the note recognition module <b>830</b> is configured to receive the visual representation and determine a general boundary of a note from the visual representation. The note extraction module <b>840</b> is configured to extract content of the one of the plurality of notes from the visual representation based on the determined general boundary of the one of the plurality of notes. The note labeling module <b>860</b> is configured to label the one of the plurality of notes with a category.

In some embodiments, not covered by the claimed invention, the note management system <b>800</b> includes a note authentication module <b>850</b> that is configured to authenticate the plurality of notes. In some cases, at least some of the plurality of notes have a mark. The mark can be used to facilitate the recognition, extraction and authentication of the note. For example, the note authentication module <b>850</b> can obtain an authentication signal from a mark of a note and verify the authentication based on the authentication signal. In some cases, the note authentication module <b>850</b> is coupled to the note recognition module <b>830</b> and provides the authentication information to the note recognition module <b>830.</b> In some embodiments, the note recognition module <b>830</b> and the note extraction module <b>840</b> can perform the steps illustrated in <figref idref="f0008">Figure 6B</figref> and in the relevant discussions. In some embodiments, not covered by the claimed invention, the note labeling module <b>860</b> can use one or more labeling approaches illustrated in <figref idref="f0017">Figure 10B</figref> and in the relevant discussions. In some cases, a note may be associated with more than one category.

In some embodiments, not covered by the claimed invention, the note management system <b>800</b> may include a note presentation module <b>870,</b> which is configured to gather content of the plurality of notes and present at least part of the plurality of notes according to the category of the notes. For example, the note presentation module <b>870</b> can organize the plurality of notes into groups that have the same category. As another example, the note presentation module <b>870</b> can add connection lines and/or arrows of different groups of notes.

<figref idref="f0018">Figure 12A</figref> illustrates an example of content extraction process of a segmented note with marks. A visual representation <b>920A</b> of a note <b>910A</b> is captured by an imaging device (e.g., a smart phone <b>922A</b>)<b>.</b> The note <b>910A</b> is segmented into four sections using four set of marks, <b>912A, 914A, 916A,</b> and <b>918A.</b> Each of the four segments has a corresponding content, Content A <b>950A,</b> Content B <b>960A,</b> Content C <b>970A,</b> and Content D <b>980A.</b> The computing device can use any of the note extraction approach to extract the note content and present to a user, as illustrated in <b>930A.</b> In addition, a category can be associated with a specific mark, as illustrated as <b>924A.</b> Using the category association, the pieces of content, Content A-D, are associated with a category as shown in <b>930A.</b>

<figref idref="f0018">Figure 12B</figref> illustrates an example of content extraction process of a note <b>910B</b> with color segments. <b>920B</b> is a visual representation of the note <b>910B</b> captured by an image sensor, for example, a smart phone <b>922B</b>. A set of content <b>940B,</b> each piece of content corresponding to a segment, are extracted using any of the note extraction process as described above. In addition, a piece of content can be associated with a category depending on the color of the color segments, and the list of content presented to a user can be grouped by the categories, as illustrated in <b>930B.</b>

<figref idref="f0019">Figure 12C</figref> illustrates an exemplary scenario of marking a board <b>910D</b> to extract segmented content. Marks <b>915D</b> are attached to board <b>910D.</b> In some cases, the relative positions (e.g, top left corner, bottom right corner) of the marks are known and used to extract content. The pieces of content <b>920D, 930D, 940D</b> and <b>950D</b> can be extracted using, for example, the pseudo code listed in Table 5, where the marks <b>915D</b> are identified as bounding boxes. <tables id="tabl0005" num="0005"><table frame="all"><title>Table 5. Pseudo Code for Extracting Content</title><tgroup cols="2" colsep="0" rowsep="0"><colspec colnum="1" colname="col1" colwidth="38mm" /><colspec colnum="2" colname="col2" colwidth="128mm" colsep="1" /><tbody><row><entry namest="col1" nameend="col2" align="left">Use color classification algorithm to identify bounding boxes and place in a collection</entry></row><row><entry namest="col1" nameend="col2" align="left">While</entry></row><row>&lt;entry /&gt;<entry>Search bounding box collection for bounding box with the minimum X value (left most)</entry></row><row>&lt;entry /&gt;<entry>Search bounding box collection for bounding box that is to the right and lower than note found in previous step (constraint is that note must be at least a defined distance to the right +X)</entry></row><row>&lt;entry /&gt;<entry>Used bounding boxes to define the region of information to be extracted</entry></row><row>&lt;entry /&gt;<entry>Discard both bounding boxes from collection</entry></row><row rowsep="1"><entry namest="col1" nameend="col2" align="left">Notes left in collection</entry></row></tbody></tgroup></table></tables>

This simple algorithm can be very effective in providing a method for extracting content from free flowing discussions on a board.

<figref idref="f0020">Figures 13A and 13B</figref> illustrate an example of extracting colored notes using color classification algorithms. <figref idref="f0020">Figure 13A</figref> illustrates a visual representation 1000A of notes with different colors, where Y representing yellow, M representing magenta, and B representing blue. <figref idref="f0020">Figure 13B</figref> represents extracting all yellow notes 1000B from the visual representation using, for example, the pseudo code listed in Table 2.

The present invention should not be considered limited to the particular examples and embodiments described above, as such embodiments are described in detail to facilitate explanation of various aspects of the invention. The invention is defined by the claims.