String-level learning of confidence transformation for Chinese handwritten text recognition

Handwritten text recognition systems commonly combine character classification confidence scores and context models for evaluating candidate segmentation-recognition paths, and the classification confidence is usually optimized at character level. On comparing the performance of class-dependent and class-independent confidence transformation (CT), this paper proposes two regularized class-dependent CT methods, and particularly, a string-level confidence learning method under the Minimum Classification Error (MCE) criterion. In experiments of online Chinese handwritten text recognition, the string-level confidence learning method was shown to effectively improve the recognition performance.