Offline Cursive Handwriting Recognition System based on Hybrid Markov Model and Neural Networks

Abstract

An offline cursive handwritten recognition system, based on hybrid of Neu Networks (NN) and Hidden markov Models (HMM), is decribed in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmantation to the recognition stage by generating a segmentation graph that decribes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates SCs in the segmentation graph. Then, using concatenated letters-HMMs, a likelihood is computed for each word in the lexicon by multiplying the possibilities over the best paths through the graph. We present in detail two approaches to train the word recognizer:1)character-level training 2) word-level training. The recognigtion performance of the two systems are discussed.