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M.Y. Chen, A. Kundu, J. Zhou, "OffLine Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 481496, May, 1994.  
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@article{ 10.1109/34.291449, author = {M.Y. Chen and A. Kundu and J. Zhou}, title = {OffLine Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {5}, issn = {01628828}, year = {1994}, pages = {481496}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.291449}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  OffLine Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network IS  5 SN  01628828 SP481 EP496 EPD  481496 A1  M.Y. Chen, A1  A. Kundu, A1  J. Zhou, PY  1994 KW  character recognition; hidden Markov models; mathematical morphology; heuristic programming; image segmentation; signal detection; offline handwritten word recognition; hidden Markov model type stochastic network; totally unconstrained handwritten word recognition; morphology; heuristics based segmentation algorithm; training algorithm; Viterbi algorithm VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
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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