RECOGNITION OF UNCONSTRAINED HANDWRITTEN ENGLISH WORDS WITH CHARACTER AND LIGATURE MODELING

Abstract

In this paper, we proposed an approach for segmentation and recognition of unconstrained handwritten English words with character and ligature modeling. Viewing a handwritten word as an alternating sequence of characters and ligatures, a network of circularly interconnected hidden Markov models is constructed to model handwritten English words of indefinite length. Then the recognition problem is regarded as finding the maximal probability path in the network for given input sequence. From the path, optimal segmentation and associated character labels are obtained simultaneously without any explicit segmentation.