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The present paper proposes a segmentation-based approach to handwritten Devanagari word recognition. On the basis of the head line, a word image is segmented in to pseudo characters. Hidden Markov models are proposed to recognize the pseudo characters. The word level recognition is done on the basis of a string edit distance.
A hidden Markov model (HMM) based approach is proposed for recognition of offline handwritten Devanagari words. The histogram of chain-code directions in the image-strips, scanned from left to right by a sliding window, is used as the feature vector. A continuous density HMM is proposed to recognize a word image. In our approach the states of the HMM are not determined a priori, but are determined...
A novel hidden Markov model (HMM) for recognition of handwritten Oriya numerals is proposed. The novelty lies in the fact that the HMM states are not determined a priori, but are determined automatically based on a database of handwritten numeral images. A handwritten numeral is assumed to be a string of several shape primitives. These are in fact the states of the proposed HMM and are found using...
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