A stochastic model for handwritten word recognition using context dependency between character patterns

In a handwritten word or sentence, deformation of each character pattern often depends on that of other character patterns due to their cursiveness or the writer's characteristic. In this paper, a new word recognition method is proposed, that takes into consideration the dependency of deformation between characters. The stochastic model used in our method, which is to say a bigram model of character patterns, is constructed on the assumption that there is a Markovian property underlying in the deformation of a character string, and has a learning algorithm based on the EM algorithm. Experimental results in ZIP code recognition show the effectiveness of our method.