  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. We design a streamlined version of GoogLeNet [13],
which was original proposed for image classification in recent years with very
deep architecture, for HCCR (denoted as HCCR-GoogLeNet). The HCCR-GoogLeNet we
used is 19 layers deep but involves with only 7.26 million parameters.
Experiments were conducted using the ICDAR 2013 offline HCCR competition
dataset. It has been shown that with the proper incorporation with traditional
directional feature maps, the proposed single and ensemble HCCR-GoogLeNet
models achieve new state of the art recognition accuracy of 96.35% and 96.74%,
respectively, outperforming previous best result with significant gap.
