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This paper presents a comparative study of three feature extraction approaches for online handwritten word recognition of two major Indic scripts-Bengali and Devanagari using Hidden Markov Model (HMM). First approach uses feature extraction from whole stroke without local zone division after segmenting the word into its basic strokes. Whereas, other two approaches consider the segmentation of a word...
This paper presents two zone-based feature extraction approaches for online handwritten character recognition of two major Indic scripts-Bengali and Devanagari. Here, each stroke of an online character is divided into a number of local zones. In the first approach, named Zone wise structural and directional features (ZSD), structural and directional features are extracted for each stroke in each of...
This paper presents an online handwritten character recognition system for two major Indic scripts-Bengali and Devanagari. In this proposal, a novel approach for feature extractions is described in which each online stroke information of a character is divided into a number of local zones. For each online stroke information different structural and directional features are extracted separately in...
To take care of variability involved in the writing style of different individuals a novel approach has been proposed in this article to segment unconstrained handwritten Bangla words into characters. Online handwriting recognition refers to the problem of interpretation of handwriting input captured as a stream of pen positions using a digitizer or other pen position sensor. For online recognition...
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