An online overlaid handwritten Japanese text recognition system for small tablet

The paper presents a recognition system of online overlaid handwritten Japanese text patterns on a smart phone or baby-face tablet. The proposed system oversegments a sequence of strokes into primitive segments at candidate off-strokes between strokes using a SVM model. One or more consecutive primitive segments form a candidate character pattern, which is recognized into a list of candidate categories. Then, a segmentation and recognition candidate lattice is constructed to represent all candidate character patterns and their corresponding character classes. Finally, the optimal path is effectively found by the Viterbi search in the lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. This system incorporates feature reduction and non-character pruning to decrease the time cost per character, and semi-incremental recognition to decrease waiting time. The recognition rates on generated and collected overlaid handwritten text are 92.16% and 93.04%, respectively. The average time cost per character is not more than 0.6 s, and the average waiting time is less than 0.875 s even on an Intel Atom 1.33 GHz CPU: a low power consumption CPU for small tablets and embedded devices. Therefore, we confirm that our system recognizes online overlaid handwritten text composed of thousands of Japanese character classes with the high recognition rate without excessive waiting time.