Handwritten character recognition using multi-resolution models
First Claim
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1. A method of recognizing handwritten characters comprising:
- receiving handwritten input comprising a plurality of strokes;
computing a set of features for each stroke in the handwritten input;
storing a pre-determined number of stroke models, the number of stroke models required to define a character being inversely proportional to the number of strokes required to create the character;
storing character models for a plurality of characters, the character models being comprised of stroke models wherein the stroke models are derived by a clustering analysis of strokes collected from the plurality of characters and wherein the clustering analysis comprises;
determining a pre-defined number of stroke models;
computing, for each stroke in the handwritten input, a distance measurement between a stroke and a nearest stroke model;
separating a fixed percentage of strokes that have a highest number of modeling errors;
using the fixed percentage of strokes that have the highest number of modeling errors to derive additional stroke models by clustering; and
repeating the separating and using steps more than once; and
computing a distance measurement between strokes of the handwritten input and stroke models of a character model.
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Abstract
A storage medium (72) having stored thereon a set of instructions, which when loaded into a microprocessor (74), causes the microprocessor (74) to extract strokes from a plurality of characters (76), derive a pre-defined number of stroke models based on the strokes extracted from the plurality of character (78) and represent the plurality of characters as sequences of stroke models (80).
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Citations
15 Claims
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1. A method of recognizing handwritten characters comprising:
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receiving handwritten input comprising a plurality of strokes;
computing a set of features for each stroke in the handwritten input;
storing a pre-determined number of stroke models, the number of stroke models required to define a character being inversely proportional to the number of strokes required to create the character;
storing character models for a plurality of characters, the character models being comprised of stroke models wherein the stroke models are derived by a clustering analysis of strokes collected from the plurality of characters and wherein the clustering analysis comprises;
determining a pre-defined number of stroke models;
computing, for each stroke in the handwritten input, a distance measurement between a stroke and a nearest stroke model;
separating a fixed percentage of strokes that have a highest number of modeling errors;
using the fixed percentage of strokes that have the highest number of modeling errors to derive additional stroke models by clustering; and
repeating the separating and using steps more than once; and
computing a distance measurement between strokes of the handwritten input and stroke models of a character model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
dividing the stroke into twelve segments, having endpoints, with equal arc length;
joining the endpoints of each of the twelve segments with straight lines; and
computing angles of the straight lines that join the endpoints of each of the twelve segments.
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8. The method according to claim 1 wherein the pre-determined number of stroke models is no more than 256 stroke models.
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9. A method of handwritten character recognition using multi-resolution models, the method comprising:
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storing a plurality of character models in a plurality of memory blocks, wherein a memory block contains character models with an identical number of strokes;
determining a number of strokes in a handwritten input;
storing a pre-determined number of stroke models corresponding to the handwritten input, the number of stroke models required to define a character being inversely proportional to the number of strokes required to create the character, the character models being comprised of stroke models;
selecting an appropriate memory block corresponding to the number of strokes in the handwritten input;
calculating a distance measurement to the plurality of characters models in the appropriate memory block to create an array of distances;
sorting the array of distances to create a sorted list;
selecting at least one candidate in the sorted list; and
reporting the at least one candidate in the sorted list. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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Specification