An HMM-based Over-Segmentation Method for Touching Chinese Handwriting Recognition

The segmentation of touching characters is still a challenging problem in offline Chinese handwriting recognition. One feasible solution is through the over-segmentation strategy which maintains a high recall of correct cuts between adjacent characters and a moderate level of redundant cuts within a single character. Previous redundant cut filtering methods rely on either pure heuristics or learned geometric properties of correct cuts. In this work, we extend learning based cut filtering method from single cut level to cut sequence level by Hidden Markov Model (HMM). As a stochastic sequential modeling tool, HMM can utilize not only properties of individual cuts but also the left-to-right temporal context and spatial dependencies among a sequence of neighboring cuts. The experimental results on a large touching character dataset show that the proposed method is effective for over-segmentation and gives better performance than previous methods.