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METHOD AND APPARATUS FOR ON-LINE HANDWRITING RECOGNITION BASED ON FEATURE VECTORS THAT USE AGGREGATED OBSERVATIONS DERIVED FROM TIME-SEQUENTIAL FRAMES
METHOD AND APPARATUS FOR ON-LINE HANDWRITING RECOGNITION BASED ON FEATURE VECTORS THAT USE AGGREGATED OBSERVATIONS DERIVED FROM TIME-SEQUENTIAL FRAMES
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机译:基于特征向量的在线手写识别的方法和装置,该特征向量使用从时间序列框架得出的综合观测
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摘要
A method for on-line handwriting recognition is based on a hidden Markov model and implies the following steps: sensing real-time at least an instantaneous write position of the handwriting, deriving from the handwriting a time-conforming string of segments each associated to a handwriting feature vector, matching the time-conforming string to various example strings from a data base pertaining to the handwriting, and selecting from the example strings a best-matching recognition string through hidden-Markov processing, or rejecting the handwriting as unrecognized. In particular, the feature vectors are based on local observations derived from a single segment, as well as on compacted observations derived from time-sequential segments.
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