A Discriminative Model for On-line Handwritten Japanese Text Retrieval

This paper describes an unconstrained on-line handwritten Japanese text retrieval system from character recognition candidates. The system is based on a discriminative model which integrates the scores of character recognition, segmentation and geometric context in search and retrieval, and the parameters are trained by supervised learning. Experiments on TUAT Kuchibue database show that the proposed method can effectively improve the system performance. When the search method with the optimal threshold retrieves for a keyword consisting of two, three or four characters, its f-measure is 0.720, 0.868 or 0.923, respectively.