Patents by Inventor Krishna S. Nathan
Krishna S. Nathan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 6757647Abstract: Systems and methods are described for concisely encoding into a lexicon (or dictionary) and decoding from the lexicon regular expressions that can represent certain huge word lists that might otherwise be considered unmanageably large. Sets of words (character sequences or ‘strings’) that share certain commonalities such as a set of numbers, which share common digits, may be condensed into digital lexicons by representing the set with a regular expression. The regular expression is a string that includes meta-character, where each meta-character is a place-marker that represents a set of at least two normal characters. When accessing or searching the lexicon, the regular expressions are dynamically expanded, as needed, to the underlying, original word list. The methods disclosed are applicable to many lexicon driven language based systems such as spelling verification systems, handwriting recognition systems, speech recognition systems and the like.Type: GrantFiled: July 30, 1998Date of Patent: June 29, 2004Assignee: International Business Machines CorporationInventors: Krishna S. Nathan, Eugene H. Ratzlaff
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Patent number: 6567548Abstract: A handwriting recognition system and method whereby various character sequences (which are typically “slurred” together when handwritten) are each modelled as a single character (“compound character model”) so as to provide increased decoding accuracy for slurred handwritten character sequences. In one aspect of the present invention, a method for generating a handwriting recognition system having compound character models comprises the steps of: providing an initial handwriting recognition system having individual character models; collecting and labelling a set of handwriting data; aligning the labelled set of handwriting data; generating compound character data using the aligned handwriting data; and retraining the initial recognition system with the compound character data to generate a new recognition system having compound character models.Type: GrantFiled: January 29, 1999Date of Patent: May 20, 2003Assignee: International Business Machines CorporationInventors: Krishna S. Nathan, Michael P. Perrone, John F. Pitrelli
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Publication number: 20020067852Abstract: A handwriting recognition system and method whereby various character sequences (which are typically “slurred” together when handwritten) are each modelled as a single character (“compound character model” ) so as to provide increased decoding accuracy for slurred handwritten character sequences. In one aspect of the present invention, a method for generating a handwriting recognition system having compound character models comprises the steps of: providing an initial handwriting recognition system having individual character models; collecting and labelling a set of handwriting data; aligning the labelled set of handwriting data; generating compound character data using the aligned handwriting data; and retraining the initial recognition system with the compound character data to generate a new recognition system having compound character models.Type: ApplicationFiled: January 29, 1999Publication date: June 6, 2002Inventors: KRISHNA S. NATHAN, MICHAEL P. PERRONE, JOHN F. PITRELLI
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Patent number: 6326957Abstract: System and methods for visually displaying page information in a handwriting recording device such as a personal digital notepad (PDN) device, in which constraints exist which limit the size of a user interface display (e.g. LCD). Various methods allow a user to view detailed page information by selecting one or more available display modes which display the selected information using one or more dynamic icons. In addition, the user can view (via the display) selected portions of handwriting content of a given electronic page, thereby affording the user the opportunity to synchronize the stored handwriting data with the handwritten text.Type: GrantFiled: January 29, 1999Date of Patent: December 4, 2001Assignee: International Business Machines CorporationInventors: Krishna S. Nathan, Michael P. Perrone, John F. Pitrelli, Eugene H. Ratzlaff, Jayashree Subrahmonia
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Patent number: 6256410Abstract: A method of training a writer dependent handwriting recognition system with handwriting samples of a specific writer comprises the steps of: capturing the handwriting samples of the specific writer; segmenting the handwriting samples of the specific writer; initializing handwriting models associated with the specific writer from the segmented handwriting samples; and refining the initialized handwriting models associated with the specific writer to generate writer dependent handwriting models for use by the writer dependent handwriting recognition system. Preferably, the method also comprises the step of repeating the refining step until the writer dependent handwriting models yield recognition results substantially satisfying a predetermined accuracy threshold.Type: GrantFiled: July 30, 1998Date of Patent: July 3, 2001Assignee: International Business Machines Corp.Inventors: Krishna S. Nathan, Michael P. Perrone, Jayashree Subrahmonia
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Patent number: 5636291Abstract: A computer-based system and method for recognizing handwriting. The present invention includes a pre-processor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models.Type: GrantFiled: June 6, 1995Date of Patent: June 3, 1997Assignee: International Business Machines CorporationInventors: Eveline J. Bellegarda, Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5550931Abstract: Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another.Type: GrantFiled: May 25, 1995Date of Patent: August 27, 1996Assignee: International Business Machines CorporationInventors: Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5544264Abstract: Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another.Type: GrantFiled: May 25, 1995Date of Patent: August 6, 1996Assignee: International Business Machines CorporationInventors: Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5544261Abstract: Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another.Type: GrantFiled: May 25, 1995Date of Patent: August 6, 1996Assignee: International Business Machines CorporationInventors: Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5544257Abstract: A computer-based system and method for recognizing handwriting. The present invention includes a preprocessor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models.Type: GrantFiled: January 8, 1992Date of Patent: August 6, 1996Assignee: International Business Machines CorporationInventors: Eveline J. Bellegarda, Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5539839Abstract: Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another.Type: GrantFiled: May 25, 1995Date of Patent: July 23, 1996Assignee: International Business Machines CorporationInventors: Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5491758Abstract: Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another.Type: GrantFiled: January 27, 1993Date of Patent: February 13, 1996Assignee: International Business Machines CorporationInventors: Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan
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Patent number: 5392363Abstract: Methods and apparatus are disclosed for recognizing handwritten words in response to an input signal from a handwriting transducer. The method includes the steps of: (a) partitioning the input signal into N frames; and (b) processing words from a vocabulary model to determine, for each processed word, a probability that the word represents a written word that is conveyed by the input signal. The determined probability is a function of N letter-frame alignment probabilities and also a probability based on a grouping of the N frames into L groups, where L is a number of letters in the word. A further step (c) identifies a word having a highest determined probability as being a most-likely word that is conveyed by the input signal. The determined probability is also a function of (a) a probability based on a frequency of occurrence of words and portions of words within a selected language model; and (b) when processing a frame other than the Nth frame, a number of frames that remain to be processed.Type: GrantFiled: November 13, 1992Date of Patent: February 21, 1995Assignee: International Business Machines CorporationInventors: Tetsunosuke Fujisaki, Krishna S. Nathan
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Patent number: 5343537Abstract: Method and apparatus for automatic recognition of handwritten text based on a suitable representation of handwriting in one or several feature vector spaces(s), Gaussian modeling in each space, and mixture decoding to take into account the contribution of all relevant prototypes in all spaces. The feature vector space(s) is selected to encompass both a local and a global description of each appropriate point on a pen trajectory. Windowing is performed to capture broad trends in the handwriting, after which a linear transformation is applied to suitably eliminate redundancy. The resulting feature vector space(s) is called chirographic space(s). Gaussian modeling is performed to isolate adequate chirographic prototype distributions in each space, and the mixture coefficients weighting these distributions are trained using a maximum likelihood framework. Decoding can be performed simply and effectively by accumulating the contribution of all relevant prototype distributions.Type: GrantFiled: October 31, 1991Date of Patent: August 30, 1994Assignee: International Business Machines CorporationInventors: Eveline J. Bellegarda, Jerome R. Bellegarda, David Nahamoo, Krishna S. Nathan