Interleaved segmental method for handwriting recognition

Claim 2

An improved method for performing handwriting recognition of a handwriting sample represented by a signal sequence, utilizing one or more point oriented feature signals to hypothesize a segment of said handwriting sample giving rise to a point oriented hypotheses score wherein the improvement comprises the steps of: generating one or more segmental feature signals of said segment of said handwriting sample giving rise to a segmental hypothesis score; augmenting said point oriented hypothesis score with a segmental matching score computed using said segmental hypothesis score; and recognizing said handwriting sample based on said augmented point oriented hypothesis score.

Claim 3

A method according to claim 1 wherein said handwriting recognition system is a Hidden Markov Model based handwriting recognition system.

Claim 4

A method according to claim 1 wherein one of said segmental feature signals is based on an inter-segmental distance measure correlation metric.

Claim 5

A method according to claim 3 further comprising the step of: preprocessing said handwriting sample so that it contains a predetermined number of points.

Claim 6

An improved method for recognizing a handwriting sample on the basis of a set of alternative hypothesis scores, said alternative hypothesis scores being accumulated at each of a set of selected hypothesis scoring points falling within said handwriting sample, each alternative hypothesis score of said set of alternative hypothesis scores representing a likelihood indication of an alternate model of said handwriting sample, said handwriting sample having one or more pen-up instances, each pen-up instance having associated therewith a spatial distance between a pen-lift point and a pen down point, said set of selected hypothesis scoring points including said pen-lift points and said pen down points, the improvement comprising the steps of: detecting each of said pen-up instances within said handwriting sample; calculating said spatial distance associated with each of said pen-up instances, respectively; associating with each of said pen-up instances a predetermined gap feature signal indicative of whether said spatial distances are at least equal to, or less than, a predetermined gap threshold spatial distance value; adjusting said set of accumulated hypothesis scores by adding a predetermined penalty to each of said accumulated hypothesis scores of said set of alternative hypothesis scores corresponding to said pen down point, indicating a model of said handwriting sample continuous between said pen lift point and said pen down point, where said predetermined gap feature signal indicates a spatial distance equal to or greater than said threshold value and by adding a predetermined penalty to each of said accumulated hypothesis scores of said set of alternative hypothesis scores corresponding to said pen down point, indicating a model of said handwriting sample not continuous between said pen lift point and said pen down point, where said predetermined gap feature signal indicates a spatial distance less than said gap threshold value; and recognizing said handwriting sample based on said adjusted alternative hypothesis scores.

Claim 7

An improved method for recognizing a handwriting sample on the basis of a set of alternative hypothesis scores, said alternative hypothesis scores being accumulated at each of a set of selected hypothesis scoring points falling within said handwriting sample, each alternative hypothesis score of said set of alternative hypothesis scores representing a likelihood indication of an alternate model of said handwriting sample, said handwriting sample having one or more pen-up instances, each pen-up instance having associated therewith a spatial distance between a pen-lift point and a pen down point, said set of selected hypothesis scoring points including said pen-lift points and said pen down points, the improvement comprising the steps of: detecting each of said pen-up instances within said handwriting sample; calculating said spatial distance associated with each of said pen-up instances, respectively; associating with each of said pen-up instances a predetermined gap feature signal indicative of whether said spatial distances are at least equal to, or less than, a predetermined gap threshold spatial distance value; adjusting said set of accumulated hypothesis scores by adding a predetermined penalty to each of said accumulated hypothesis scores of said set of alternative hypothesis scores corresponding to said pen down point, indicating a model of said handwriting sample continuous between said pen lift point and said pen down point, where said predetermined gap feature signal indicates a spatial distance equal to or greater than said threshold value and by adding a predetermined penalty to each of said accumulated hypothesis scores of said set of alternative hypothesis scores corresponding to said pen down point, indicating a model of said handwriting sample not continuous between said pen lift point and said pen down point, where said predetermined gap feature signal indicates a spatial distance less than said gap threshold value; and recognizing said handwriting sample based on said adjusted alternative hypothesis scores.

Claim 8

A method according to claim 5 wherein said predetermined gap feature signal is a binary feature signal.

Claim 9

A method according to claim 5 wherein said handwriting recognition system is a Hidden Markov Model based handwriting recognition system.