A Candidate Lattice Refinement Method for Online Handwritten Japanese Text Recognition

This paper presents a candidate lattice refinement method for online handwritten Japanese text recognition. In the integrated segmentation-recognition framework, we first over-segment a character string pattern into primitive segments at least at their true boundaries so that each primitive segment may compose a single character or a part of a character. Then a candidate lattice is constructed based on the primitive segments. We search within the candidate lattice to obtain the optimal path as recognition result. In striving for high recognition accuracy, however, the approach must generate many candidate lattice nodes, which ultimately increase the recognition time. To solve this problem, we refine the candidate lattice to eliminate unnecessary nodes before path search and text recognition. For the refinement, we evaluate all segmentation hypotheses by combining the probability of a character verifier using noncharacter samples, the class-independent unary and binary geometric context, as well as character segmentation. We retain N-best paths by beam search to reduce the complexity of the candidate lattice. Experiments on horizontal text lines extracted from the Kondate database show that the proposed method keeps recognition accuracy while reducing recognition time to half.