Stochastic Segment Model Adaptation for Offline Handwriting Recognition

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 a segmental model was used as an additional knowledge source for re-ranking the n-best list. Here, we describe a novel framework for unsupervised adaptation. It integrates both HMM and segment model adaptation to achieve significant gains over un-adapted recognition. Experimental results demonstrate the efficacy of our proposed method on a large corpus of handwritten Arabic documents.