Masaki Nagakawa

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This paper describes a robust context integration model for on-line handwritten Japanese text recognition. Based on string class probability approximation, the proposed method evaluates the likelihood of candidate segmentation–recognition paths by combining the scores of character recognition, unary and binary geometric features, as well as linguistic(More)
This paper describes a method for online handwritten Lao character recognition by using dynamic programming matching (DPM). It extracts feature points along the way from pen-down to pen-up, then uses DPM to match those feature points with the feature points for a template pattern of each character class and obtains a similarity for each character class. It(More)
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