Fast self-generation voting for handwritten Chinese character recognition

In this paper, a fast self-generation voting method is proposed for further improving the performance in handwritten Chinese character recognition. In this method, firstly, a set of samples are generated by the proposed fast self-generation method, and then these samples are classified by the baseline classifier, and the final recognition result is determined by voting from these classification results. Two methods that are normalization-cooperated feature extraction strategy and an approximated line density are used for speeding up the self-generation method. We evaluate the proposed method on the CASIA and CASIA-HWDB1.1 databases. High recognition rate of 98.84 % on the CASIA database and 91.17 % on the CASIA-HWDB1.1 database are obtained. These results demonstrate that the proposed method outperforms the state-of-the-art methods and is useful for practical applications.