An MQDF-CNN Hybrid Model for Offline Handwritten Chinese Character Recognition

An MQDF-CNN hybrid model is presented for offline handwritten Chinese character recognition. The main idea behind MQDF-CNN hybrid model is that the significant difference on features and classification mechanisms between MQDF and CNN can complement each other. Linear confidence accumulation and multiplication confidence criteria are used for fusion outputs of MQDF and CNN. Experiments have been conducted on CASIA-HWDB1.1 and ICDAR2013 offline handwritten Chinese character recognition competition dataset. On both datasets, CNN beats MQDF by more than 1% of the accuracy, and the MQDF-CNN hybrid model has achieved the test accuracies of 92.03% and 94.44% respectively. The result on competition dataset is comparable to the state-of-the-art result though less training samples and only one CNN is used.