High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps

Understand

Just like its great success in solving many computer vision problems, the convolutional neural networks (CNN) provided new end-to-end approach to handwritten Chinese character recognition (HCCR) with very promising results in recent years.

• However, previous CNNs so far proposed for HCCR were neither deep enough nor slim enough.
• We show in this paper that, a deeper architecture can benefit HCCR a lot to achieve higher performance, meanwhile can be designed with less parameters.
• We also show that the traditional feature extraction methods, such as Gabor or gradient feature maps, are still useful for enhancing the performance of CNN.