Abstract: A method of classifying a character string formed from a known number of hand-written characters is disclosed. The method starts by determining character probabilities for each hand-written character in the character string. Each character probability represents a likelihood of the respective hand-written character being a respective one of a plurality of predetermined characters. Each predetermined character has a respective character type. Character templates having the known number of characters are next identified. Each character template has a respective predetermined probability and represents a respective combination of character types. Character sequence probabilities corresponding to each of the character templates having the known number of characters are next determined. The character sequence probabilities are a function of the predetermined probability of the respective character template and the character probabilities of the hand-written character in the character string.

Abstract: A character recognizing system includes a portable electronic device, a location sensing system and a server system. The portable electronic device captures image of a target to produce a captured image. The location sensing system locates position of the portable electronic device to produce a position information. The server system receives the captured image and the position information via internet for executing recognizing motion.

Abstract: A communication system and device that enables free-hand drawn SMS (Short Messaging Service) messages to be transmitted and received from/to various user devices. A buffer device is inserted within a GSM compatible handset providing a buffer for both conventional SMS messages created by typing a message on the keypad of the handset and for free-hand drawn SMS messages created by drawing or writing the free-hand message on a data entry device. An optional OCR (Optical Character Recognition) facility can be provided in either the buffer device inserted within the handset or in a network server that receives the transmitted message and processes it for proper routing to the intended recipient.

Abstract: A method for determining the orientation of Chinese words is provided. The amount of dark pixels in each column of a Chinese word image is calculated. Then, a first point, a second point, and a third point are determined. The first point and the second point correspond to the columns with the largest and the second largest amount of dark pixels, respectively. The third point is located between the first point and the second point. The Chinese word is right-side up if the third point is located on the left side of the Chinese word. The Chinese word is upside down if the third point is located on the right side of the Chinese word.

Abstract: In an entry assistance apparatus, when the user makes handwritten entry on a medium, a detector detects person condition information on a condition regarding the body of the user. The person condition information may include either or both of positional information on the position of the user and postural information on the posture of the user. Based on the person condition information, a condition estimator estimates one or more conditions of the user. Based on the condition of the user, a presentation information selector selects presentation information on one or more information contents to be presented to the user. The information content indicated by the presentation information will be presented to the user by an information presenter.

Abstract: A method begins by receiving an image of a handwritten item. The method performs a word segmentation process on the image to produce a sub-image and extracts a set of feature vectors from the sub-image. Then, the method performs an asymmetric approach that computes a first log-likelihood score of the feature vectors using a word model having a first structure (such as one comprising a Hidden Markov Model (HMM)) and also computes a second log-likelihood score of the feature vectors using a background model having a second structure (such as one comprising a Gaussian Mixture Model (GMM)). The method computes a final score for the sub-image by subtracting the second log-likelihood score from the first log-likelihood score. The final score is then compared against a predetermined standard to produce a word identification result and the word identification result is output.

Abstract: A method of modifying a classification scheme for classifying hand-written characters. The method includes the steps of: receiving digital ink representing a handwritten character from an optically imaging pen; determining a selection value based on the similarity of the received character and a respective prototype vector of the stored classification scheme; selecting a prototype vector of the stored classification scheme in accordance with the determined selection value; selecting a class of the stored classification scheme in accordance with a selected prototype representing the character; selecting a stored user dependent weighted allograph in accordance with the selected prototype vector representing the character; modifying an allograph in the respective class; and storing a modified classification scheme which includes the modified allograph.

Abstract: A printer, scanner device and methods for using same are described herein. A printer device may include a dedicated input that, when actuated, generates and sends a request to a computer for known data or a predetermined print job, e.g., schedule information from a personal information management (PIM) application. A scanner device may include another dedicated input that, when actuated, automatically scans a document fed to the device by the user and sends the scanned image to IM (or other) software on a computer, bypassing the need to manipulate the scanned image using scanner software. The device may be used with printed metapaper, which includes a barcode or other indicia identifying the metapaper and corresponds to a stored template image of the metapaper. When the metapaper is rescanned, the scan can be compared to the stored template information to identify changes and synchronize the changes with the IM software.

Abstract: In one embodiment, a method for encoding source data includes defining a source polygon using a parametric representation associated with the source data, and identifying one or more portions of the source polygon. The method further includes passing a filter throughout the entire sequence of the samples representing the source polygon, and performing a piecewise, portion by portion, re-parameterization of the source polygon using the corresponding portions of the filtered polygon to obtain an updated sequence of samples representing the source polygon. The passing of the filter and the performance of the piecewise re-parameterization are then iteratively repeated until a convergence condition is satisfied, to provide encoded data.

Abstract: A method for classifying a handwritten input character is disclosed. Character models are used. Each character model is associated with an output character and defines a model specific segmentation scheme for that output character and an associated segment model. The model specific segmentation scheme defines a minimum length corresponding to a number of points in a stroke of the output character and a minimum length threshold. Using each of the character models, the input character is decomposed into segments and the segments are evaluated against the segment model of the respective character model to produce a score indicative of the conformity of the segments with the segment model. The character model that produced the highest score is selected and the input character is classified as the output character associated with the character model that produces the highest score.

Abstract: A cursive character handwriting recognition system includes image processing means for processing an image of a handwritten word of one or more characters and classification means for determining an optimal string of one or more characters as composing the imaged word. The processing means segments the characters such that each character is made up of one or more segments and determines a sequence of the segments using an over-segmentation-relabeling algorithm. The system also includes feature extraction means for deriving a feature vector to represent feature information of one segment or a combination of several consecutive segments. The over-segmentation-relabeling algorithm places certain segments considered as diacritics or small segments so as to immediately precede or follow a segment of the associated main character body.

Abstract: A method of classifying a character string formed from a known number of hand-written characters is disclosed. The method starts by determining character probabilities for each hand-written character in the character string. Each character probability represents a likelihood of the respective hand-written character being a respective one of a plurality of predetermined characters. Each predetermined character has a respective character type. Character templates having the known number of characters are next identified. Each character template has a respective predetermined probability and represents a respective combination of character types. Character sequence probabilities corresponding to each of the character templates having the known number of characters are next determined. The character sequence probabilities are a function of the predetermined probability of the respective character template and the character probabilities of the hand-written character in the character string.

Abstract: An exemplary method for handwritten character generation includes receiving one or more characters and, for the one or more received characters, generating handwritten characters using Hidden Markov Models trained for generating handwritten characters. In such a method the trained Hidden Markov Models can be adapted using a technique such as a maximum a posterior technique, a maximum likelihood linear regression technique or an Eigen-space technique.

Abstract: Various technologies and techniques are disclosed that improve cursive handwriting recognition. Cursive handwriting input is received from a user. The system performs a hierarchical prototype search as part of a recognition operation. A same space search is performed against a mixed database that has both print and cursive samples. A same space search is also performed against a cursive database that has only cursive samples. The results of these two same space searches are merged into a combined alternate list. The combined alternate list is then used as a constraint for the dynamic time warp searches that are performed against the mixed and cursive databases, respectively. The results of the dynamic time warp searches are also merged into a final combined alternate list, and the combined alternate list is used to make a recognition decision regarding the user's handwritten input.

Abstract: A practical and natural way of inputting syllables of scripts into a computer. In one example embodiment, This is achieved by selecting a base character from a set of characters using a digitizing tablet [1216]. The selected base character is then modified by drawing one or more natural handwritten modifying gestures to form a current desired syllable. An associated data of the formed current desired syllable is then inputted into a gesture-keypad-engine [1230] via the digitizing tablet [1216] upon completion of the drawing of the one or more natural handwritten modifying gestures. The gesture-keypad-engine [1230] then produces a current candidate syllable as a function of the inputted associated data of the formed current desired syllable. The produced current candidate syllable is then displayed on a display device [540].

Abstract: A memory footprint of an Modified Quadratic Discriminant Function (MQDF) pattern recognition classifier is reduced without resulting in unacceptable classification accuracy degradation. Covariance matrices for multiple classes are clustered into a smaller number of matrices where different classes share the same set of eigenvectors. According to another approach, different numbers of principal components are stored for different classes based on criteria such as class usage frequency, larger variation in writing, and the like, resulting in fewer principal components to be stored in memory.

Abstract: An information system for capturing, processing, and transmitting recorded information via writing devices is described. The system comprises computing devices, an application stored on a stand-alone computer or on an application server, and client devices, which execute the application as well as collect and store data. The computing device for capturing the data is preferably a digital pen. A data collector includes specifically designed forms or a graphic user interface (GUI) that enables the collection of vital information. A data storage module stores vital information processed by the application. The computing device and the client device are usable online or offline a network. The client device is selected from the group consisting of a computer, printer, scanner, or other digital devices and is usable online or offline a network.

Abstract: Methods and systems for incorporating handwritten information into a text or graphics document as text data, image data or command cues received through a positional input digitizing recognizer, such as a digitizer pad. In a data entry mode, a unit of data is inserted and accepted for placement at a designated location. In a recognition mode, the data is recognized as legible text with optional embedded edit or other commands, or it is recognized as graphics and accepted into an associated image frame. Information interpreted as commands can be executed as editing commands to control addition, deletion or movement of text within the document, as well as font or type size change or color change and the like. The invention may take advantage of features native to the particular application.

Abstract: A method of interpreting handwritten data inputted on a printed form using an optically sensing pen. The form has a plurality of different form fields for the handwritten data input. The form also includes a plurality of printed tags and the method includes the steps of: (a) receiving, from the pen, digital ink representing handwritten data input of the pen relative to the form; (b) identifying the form field in which handwritten data has been inputted; (c) identifying a set of predetermined text values associated with the identified form field; and (d) interpreting the handwritten data by decoding the digital ink and converting the decoded digital ink into computer text. The computer text is selected only from the set of predetermined text values.

Abstract: A method for classifying digital ink receives digital ink comprising ink strokes. A plurality of the ink strokes can be classified. A temporal line grouping is performed on a plurality of the classified ink strokes that are grouped to form a temporal line group. The temporal line group is segmented into a cluster. The cluster can be classified.

Abstract: A method of modifying a classification scheme for classifying hand-written characters. The method includes the steps of: receiving digital ink representing a handwritten character from an optically imaging pen; determining a selection value based on the similarity of the received character and a respective prototype vector of the stored classification scheme; selecting a prototype vector of the stored classification scheme in accordance with the determined selection value; selecting a class of the stored classification scheme in accordance with a selected prototype representing the character; selecting a stored user dependent weighted allograph in accordance with the selected prototype vector representing the character; modifying an allograph in the respective class; and storing a modified classification scheme which includes the modified allograph.

Abstract: A handwritten information management system, which includes a paper sheet document generation section that generates a paper sheet document in which plurality pieces of coded information including coordinate information indicating a position on a paper sheet and document identification information of the paper sheet document formed on the paper sheet are recorded; a paper sheet document registration section that registers the generated paper sheet document and an electronic document corresponding to the paper sheet document correlated with the document identification information; a handwritten information detection section that detects handwritten information handwriting on the generated paper sheet document; a handwritten information registration section that registers handwritten information identification information to identify the handwritten information correlated with the detected handwritten information; and an identification information management section that manages the document identification info

Abstract: A handwriting processing apparatus and method effective for search of, e.g., a document file including handwriting is provided. When a handwriting characters are input to a coordinate input unit as a search key, a corresponding character in a dictionary is recognized for each of the handwritten characters, a search unit searches for a text code stored in a document file storage unit based on a text code of the corresponding character if the corresponding character is recognized and the search unit searches for handwriting trail data stored in a handwriting trail storage unit based on a handwriting trail of the handwriting character if the corresponding character is not recognized, thereby finding a desired document file.

Abstract: Various technologies and techniques are disclosed that improve cursive handwriting recognition. Cursive handwriting input is received from a user. The system performs a hierarchical prototype search as part of a recognition operation. A same space search is performed against a mixed database that has both print and cursive samples. A same space search is also performed against a cursive database that has only cursive samples. The results of these two same space searches are merged into a combined alternate list. The combined alternate list is then used as a constraint for the dynamic time warp searches that are performed against the mixed and cursive databases, respectively. The results of the dynamic time warp searches are also merged into a final combined alternate list, and the combined alternate list is used to make a recognition decision regarding the user's handwritten input.

Abstract: A mechanism for recognizing and inputting handwritten mathematical expressions into a computer by providing part of a multi-path framework is described. The part of the multi-path framework includes a subordinate sub-expression analysis component. A method for analyzing a handwritten mathematical expression for a subordinate sub-expression includes identifying sub-expressions based on dominant symbols and determining a character for potential dominant symbols based upon sub-expression information. A determination may be made whether an expression structure candidate is valid and valid expression structure candidates may be stored in a parse tree.

Abstract: Computer-readable media having computer-executable instructions distinguish the script type of at least one portion of a writing input. At least one sub-word of a writing line of a handwritten document is identified and is processed to determine the associated writing style that includes a cursive writing style and a hand-printed writing style. The writing line is consequently associated with a script type. The script type of a writing line is determined from the script types of the sub-words in the writing line. When the number of sub-words having a first script type is greater than the number of sub-words having a second script type, the script type of the writing line is categorized as the first script type. In addition, a script analyzer determines a writing style of at least one sub-word and selects one of a plurality of neural networks to categorize the script type of a writing line.

Abstract: Signature data of a handwritten signature input to a signature authentication device is analyzed to determine whether to register the signature based on the characteristics of the stroke shape of the signature. If the signature registration is denied, a response message for making the signature registerable is displayed according to the way in which the signature is written. At least one of cumulative angle changes in the locus of the signature, fluctuation in the speed at which the signature was written, fluctuation in the size of the characters included in the signature, and fluctuation in the center positions of the characters included in the signature is used as the determination criterion.

Abstract: A handwriting compound system is provided. The system includes a handwriting recognition module that receives a proper number of letters from a user, detects a distance between phonemes/syllables, and shapes and locations of the phonemes/syllables, and recognizes the user's handwriting. The system also includes a point designating module, a reference point setting module and a handwriting transforming module. The point designating module designates a particular one of the subdivided positions in each phoneme recognized by the handwriting recognition module. The reference point setting module sets a reference point, serving as reference for the alteration of a phoneme/syllable, inside or outside the user's input phoneme/syllable recognized by the handwriting recognition module. The handwriting transforming module designates x and y coordinates of a first point with respect to the reference point.

Abstract: Biometric data are obtained through a biometric input device (120) and subsequently pixelated via a pixelator (130). The pixelator (130) creates an image of the biometric data. Via a processing unit (110), a relationship between pixels of a transformed version of the image is asserted. Thus, the biometric data is rotated to a consistent inclination based on the relationship between pixels regardless of an orientation in which the biometric data were captured in the original image. Once the image has been transformed, features of the biometric data may be extracted and either stored in a data storage unit (140) or compared with previously stored feature values for validation of the biometric data.

Abstract: Systems, methods, and computer-readable mediums for determining authorship of a handwritten document for which the authorship is not known. A method includes scanning a document to produce a high-quality scanned image of the document, and identifying stylus information corresponding to the document. The method includes identifying authorship information corresponding to the document, and determining an authorship of the document based on the stylus information and the authorship information. In some cases, content analysis of the document is also performed and used to determine authorship.

Abstract: A system for modifying a classification scheme for classifying hand-written characters. A memory stores the classification scheme, which includes a number of allographs, each allograph representing a respective style of a respective letter. A processor received data representing a hand-written character, and then selects an allograph representing the character. One or more of the allographs are then modified in accordance with the selection, to thereby reflect the fact that the allograph corresponds to a style of letter used by a user.

Abstract: The present invention provides a method for applying a signature simplicity analysis for improving the accuracy of signature validation, the method including the steps of generating a plurality of synthetic fraudulent signatures for a person, encoding authentic signatures of the person using signature simplicity and validating the signatures using signature simplicity.

Abstract: A method for conducting a transaction over an electronic network may comprise receiving transaction data, receiving biometric data including a relational check code representative of the biometric data, a date time stamp, an identifier of the hardware that recorded the biometric data, or a combination thereof, comparing the biometric data with biometric data previously stored, or with a predetermined threshold value, or with a combination thereof, for authenticating the biometric data for approving or disapproving the transaction. If approved, the transaction data is processed for conducting the transaction. Biometric data may include a locus-based digitized signature, a biometric digital signature, a fingerprint, a palm print, hand geometry, facial geometry, an iris scan, an iris print, a retinal scan, a retinal print, an eye scan, an eye print, or any combination thereof.

Abstract: A method of estimating the orientation of digital ink is provided. The includes measuring, at a sampling rate during writing by an optically imaging pen on a surface printed with tags, the azimuth of the pen, and estimating, at the computer system, the orientation of the segment of digital ink using the measured azimuth of the pen at the sampled points. Each tag encodes data on an identity of the surface associated with a digital description of the surface and on the respective location of that tag on the surface. The digital description is stored by a computer system networked with the pen and the digital ink is generated by associating the digital description with the data encoded by the tags optically imaged by the pen during the writing.

Abstract: A system for estimating the orientation of digital ink is provided which has an optically imaging pen and a processor. The processor measures the azimuth of the pen at a sampling rate during writing by the pen on a surface printed with tags and estimates the orientation of the digital ink using the measured azimuth of the pen at the sampled points. Each tag encodes data on an identity of the surface associated with a digital description of the surface and on the respective location of that tag on the surface. The digital ink is generated by associating the digital description with the data encoded by the tags optically imaged by the pen during the writing.

Abstract: The present invention provides a Chinese character search method. According to the method, the user first inputs the notation of the known character. When the input notation is received, some corresponding Chinese characters are shown in the display. Then, the user chooses the correct character for which he is searching. Next, symbols are added to this character to represent the unknown Chinese character in the word. Then, those words containing this character are searched and are shown in a display.

Abstract: A forward pass through a sequence of strokes representing a handwritten equation is performed from the first stroke to the last stroke in the sequence. At each stroke, a path score is determined for a plurality of symbol-relation pairs that each represents a symbol and its spatial relation to a predecessor symbol. A symbol graph having nodes and links is constructed by backtracking through the strokes from the last stroke to the first stroke and assigning scores to the links based on the path scores for the symbol-relation pairs. The symbol graph is used to recognize a mathematical expression based in part on the scores for the links and the mathematical expression is stored.

Abstract: A handwriting recognition apparatus is disclosed. In one embodiment the apparatus comprises an input device having a handwriting input area and configured to input a plurality of strokes constructing a plurality of characters written successively on the handwriting input area, and a recognition device configured to recognize the characters based on the strokes input by the input device, shapes of the strokes constructing two characters which are written successively and positional relations between or among the strokes constructing the two characters, whenever one stroke is input by the input device.

Abstract: Various technologies and techniques are disclosed for using user corrections to help improve handwriting recognition operations. The system tracks user corrections to recognition results. The system receives handwritten input from the user and performs a recognition operation to determine a top recognized word. The prior corrections made by the user are analyzed to calculate a ratio of times the user has corrected the top recognized word to a particular other word as opposed to correcting the particular other word to the top recognized word. If the ratio meets or exceeds a required minimum, then at least one secondary source is optionally analyzed to determine if the particular other word is used a certain number of times more frequently than the top recognized word in the secondary source. The system performs a swap of the top recognized word with the particular other word when the required criteria are met.

Abstract: A plurality of users may access a processing unit operable for the development of a model, and model development instructions from the users may be received at the processing unit. The model may be developed in a plurality of steps using the model development instructions. Signoff instructions corresponding to the steps for developing the model may be received at the processing unit, each of the signoff instructions being received after each corresponding step is performed, with each of the plurality of signoff instructions indicating approval or denial. The respective step may be approved by the processing unit if the corresponding signoff instruction indicates approval, and rejected by the processing unit if the corresponding signoff instruction indicates denial. The processing unit is operable as a central location accessible by the users for developing, and signing off on the development of, the model according to the development instructions and the signoff instructions.

Abstract: A method of identifying a string formed from a number of hand-written characters is disclosed. The method starts by determining character probabilities for each hand-written character in the string. Each character probability represents the likelihood of the respective hand-written character being a respective one of a number of predetermined characters. Next, template probabilities for the string are determined. Each template probability represents the likelihood of the string corresponding to a respective one of a number of templates. Each template represents a respective combination of character types. The step of determining the template probabilities for the string includes the sub-steps of determining the number of characters in the string, selecting templates having an identical number of characters, and obtaining a template probability for each selected template.

Abstract: A method identifying a ligature within a scanned document, the ligature including two or more touching characters. The two or more touching characters of the ligature are then compared to a plurality of prototypes to identify two or more matched prototypes. A synthetic ligature is then created based on the two or more matched prototypes.

Abstract: Handwriting recognition techniques employing a personalized handwriting recognition engine. The recognition techniques use examples of an individual's previous writing style to help recognize new pen input from that individual. The techniques also employ a shape trainer to select samples of an individual's handwriting that accurately represent the individual's writing style, for use as prototypes to recognize subsequent handwriting from the individual. The techniques also alternately or additionally employ an intelligent combiner to combine the recognition results from the personalized recognition engine and the conventional recognition engine (or engines). The combiner may use a comparative neural network to combine the recognition results from multiple recognition engines. The combiner alternately may use a rule-based system based on prior knowledge of different recognition engines.

Abstract: A method for recognition of a handwritten pattern comprising one or more curves is presented. The method comprises a step of receiving sample data representing the handwritten pattern. The method further comprises a step of segmenting the handwritten pattern by detecting segmentation points on each curve, and by dividing the handwritten pattern into segments. Further, the method comprises a step of comparing the handwritten pattern to templates wherein the comparing comprises a step of normalizing said segments according to a scheme which is independent of the templates to which the segments are to be compared, and a step of determining matching measures for selecting at least one sequence of templates representing a recognintion candidate of the handwritten pattern.

Abstract: Systems, methods, and computer-readable media for processing electronic ink: (a) receive electronic ink input; (b) convert the input to machine-generated objects; and (c) render the objects such that their size substantially corresponds to the input's original size. The input ink may constitute text, and the machine-generated objects may correspond to words, lines, and/or other groupings of text generated by a handwriting recognizer. To enable quick and easy identification of recognizer errors, in at least some systems and methods, a user may select one or more words, which will cause the system to display the original ink corresponding to the selected word(s). Such systems also may display alternative words generated by the recognizer corresponding to the selected original ink and allow the user to select one of the alternatives to make corrections in the recognized text (akin to using a spell-checking or handwriting recognition program).

Abstract: A method for classifying an input character is disclosed. Character models are used. Each character model is associated with an output character and defines a model specific segmentation scheme for that output character and an associated segment model. The model specific segmentation scheme defines a minimum length corresponding to a number of points in a stroke of the output character and a minimum length threshold. Using each of the character models, the input character is decomposed into segments and the segments are evaluated against the segment model of the respective character model to produce a score indicative of the conformity of the segments with the segment model. The character model that produced the highest score is selected and the input character is classified as the output character associated with the character model that produces the highest score.

Abstract: A method and system for implementing character recognition is described herein. An input character is received. The input character is composed of one or more logical structures in a particular layout. The layout of the one or more logical structures is identified. One or more of a plurality of classifiers are selected based on the layout of the one or more logical structures in the input character. The entire character is input into the selected classifiers. The selected classifiers classify the logical structures. The outputs from the selected classifiers are then combined to form an output character vector.

Abstract: Provided is a handwriting storage method in a computing device capable of analog input. The handwriting storage method receives handwriting through an analog input unit, decomposes the handwriting into at least one vector, and stores the at least one vector as a straight-type character.

Abstract: A system for modifying a classification scheme for classifying hand-written characters. A memory stores the classification scheme, which includes a number of allographs, each allograph representing a respective style of a respective letter. A processor received data representing a hand-written character, and then selects an allograph representing the character. One or more of the allographs are then modified in accordance with the selection, to thereby reflect the fact that the allograph corresponds to a style of letter used by a user.

Abstract: A method and system for performing computer implemented recognition is disclosed. In one method embodiment, the present invention first accesses user input stored in a memory of a mobile device. On the mobile device, the present invention performs a coarse recognition process on the user input to generate a coarse result. The coarse process may operate in real-time. The embodiment then displays a portion of the coarse result on a display screen of the mobile device. The embodiment further performs a detailed recognition process on the user input to generate a detailed result. The detailed process has more recognition patterns and computing resources available to it. The present embodiment performs a comparison of the detailed result and the coarse result. The present embodiment displays a portion of the comparison on the display screen.