System and method for superimposed handwriting recognition technology
First Claim
1. A system for providing handwriting recognition for a plurality of at least partially superimposed fragments of input strokes on a computing device, the computing device comprising a processor and at least one non-transitory computer readable medium under control of the processor, the at least one non-transitory computer readable medium configured to:
- determine the time order of input of at least sequential fragments;
detect the geometry of the input strokes in the at least sequential fragments;
detect the relative positions of the input strokes of the at least sequential fragments;
determine from the determined time order and detected relative positions and geometries whether one or more of the input strokes of the at least sequential fragments combine to form one or more likely characters;
classify the fragments based on the determined likely characters; and
provide the classified fragments to a recognition engine for evaluation of character hypotheses based on the classified fragments,wherein, within the recognition engine, a method comprises;
creating a segmentation graph based on the strokes of the classified fragments, wherein the segmentation graph includes nodes corresponding to character hypotheses;
assigning a recognition score to each node of the segmentation graph based on a pattern classifier; and
generating linguistic meaning of the input strokes based on the recognition scores and a language model.
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Abstract
A system and method that is able to recognize a user'"'"'s natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. It can also process cursive handwriting. Further, the method and system can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of fragmentation, segmentation, character recognition, and language modeling. At least some of these processes occur concurrently through the use of dynamic programming.
69 Citations
18 Claims
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1. A system for providing handwriting recognition for a plurality of at least partially superimposed fragments of input strokes on a computing device, the computing device comprising a processor and at least one non-transitory computer readable medium under control of the processor, the at least one non-transitory computer readable medium configured to:
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determine the time order of input of at least sequential fragments; detect the geometry of the input strokes in the at least sequential fragments; detect the relative positions of the input strokes of the at least sequential fragments; determine from the determined time order and detected relative positions and geometries whether one or more of the input strokes of the at least sequential fragments combine to form one or more likely characters; classify the fragments based on the determined likely characters; and provide the classified fragments to a recognition engine for evaluation of character hypotheses based on the classified fragments, wherein, within the recognition engine, a method comprises; creating a segmentation graph based on the strokes of the classified fragments, wherein the segmentation graph includes nodes corresponding to character hypotheses; assigning a recognition score to each node of the segmentation graph based on a pattern classifier; and generating linguistic meaning of the input strokes based on the recognition scores and a language model. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for providing handwriting recognition for a plurality of at least partially superimposed fragments of input strokes on a computing device, the computing device comprising a processor and at least one non-transitory computer readable medium for recognizing the handwriting under control of the processor, the method comprising:
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determining the time order of input of at least sequential fragments; detecting the geometry of the input strokes in the at least sequential fragments; detecting the relative positions of the input strokes of the at least sequential fragments; determining from the determined time order and detected relative positions and geometries whether one or more of the input strokes of the at least sequential fragments combine to form one or more likely characters; classifying the fragments based on the determined likely characters; providing the classified fragments to a recognition engine for evaluation of character hypotheses based on the classified fragments; creating a segmentation graph based on the strokes of the classified fragments, wherein the segmentation graph includes nodes corresponding to character hypotheses; assigning a recognition score to each node of the segmentation graph based on a pattern classifier; and generating linguistic meaning of the input strokes based on the recognition scores and a language model. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer readable medium having computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for a plurality of at least partially superimposed fragments of input strokes on a computing device, the computing device comprising a processor, the non-transitory computer readable medium under control of the processor implementing the method comprising:
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determining the time order of input of at least sequential fragments; detecting the geometry of the input strokes in the at least sequential fragments; detecting the relative positions of the input strokes of the at least sequential fragments; determining from the determined time order and detected relative positions and geometries whether one or more of the input strokes of the at least sequential fragments combine to form one or more likely characters; classifying the fragments based on the determined likely characters; providing the classified fragments to a recognition engine for evaluation of character hypotheses based on the classified fragments; creating a segmentation graph based on the strokes of the classified fragments, wherein the segmentation graph includes nodes corresponding to character hypotheses; assigning a recognition score to each node of the segmentation graph based on a pattern classifier; and generating linguistic meaning of the input strokes based on the recognition scores and a language model. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification