Deep-Belief-Network based Rescoring for Handwritten Word Recognition

This paper presents a novel verification approach towards improvement of handwriting recognition systems using a word hypotheses rescoring scheme by Deep Belief Networks DBNS. A recurrent neural network based sequential text recognition system is used at first to provide the N-best recognition hypotheses of word images. Word hypotheses are aligned with the word image to obtain the character boundaries. Then, a verification approach using a DBN classifier is performed for each character segments. DBNs are recently proved to be very effective for a variety of machine learning problems. The character probabilities obtained from DBNs are next combined with the base recognition system. Finally, the N-best recognition hypotheses list is reranked according to the new score. We have compared our proposed approach with an MLP based rescoring approach on the Rimes dataset The results obtained show that the verification approach using DBNs outperforms that of MLP systems.