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Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
May 1994 (vol. 16 no. 5)
pp. 481-496

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported.

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Index Terms:
character recognition; hidden Markov models; mathematical morphology; heuristic programming; image segmentation; signal detection; off-line handwritten word recognition; hidden Markov model type stochastic network; totally unconstrained handwritten word recognition; morphology; heuristics based segmentation algorithm; training algorithm; Viterbi algorithm
Citation:
M.Y. Chen, A. Kundu, J. Zhou, "Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 481-496, May 1994, doi:10.1109/34.291449
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