Minimum Error Discriminative Training for Radical-Based Online Chinese Handwriting Recognition

Free style Chinese handwriting recognition continues to pose a challenge to researchers due to the variety of writing styles. To recognize handwritten characters in an online mode, Hidden Markov Model (HMM) has been naturally adopted to model the pen trajectory of a character and a decent recognition performance is achieved. In this study, we start from a maximum likelihood trained HMM model and focus on minimizing recognition errors at the radical (sub- character) level to optimize the recognition performance. A novel Minimum Radical Error discriminative training criterion is proposed, and compared with the discrimination at the character level, our new approach further reduces the character errors by 15.6% relatively (29.0% overall reduction from the maximum likelihood baseline model) on a Chinese database.