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In this paper, we present techniques for unsupervised adaptation of stochastic segment models to improve accuracy on large vocabulary offline handwriting recognition (OHR) tasks. We build upon our previous work on stochastic segment modeling for Arabic OHR. In our previous work, stochastic character segments for each n-best hypothesis were generated by a hidden Markov model (HMM) recognizer, and then...
In this paper, we present a novel approach for incorporating structural information into the hidden Markov modeling (HMM) framework for offline handwriting recognition. Traditionally, structural features have been used in recognition approaches that rely on accurate segmentation of words into smaller units (sub-words or characters). However, such segmentation based approaches do not perform well on...
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