  Deep convolutional neural networks (DCNNs) have achieved great success in
various computer vision and pattern recognition applications, including those
for handwritten Chinese character recognition (HCCR). However, most current
DCNN-based HCCR approaches treat the handwritten sample simply as an image
bitmap, ignoring some vital domain-specific information that may be useful but
that cannot be learnt by traditional networks. In this paper, we propose an
enhancement of the DCNN approach to online HCCR by incorporating a variety of
domain-specific knowledge, including deformation, non-linear normalization,
imaginary strokes, path signature, and 8-directional features. Our contribution
is twofold. First, these domain-specific technologies are investigated and
integrated with a DCNN to form a composite network to achieve improved
performance. Second, the resulting DCNNs with diversity in their domain
knowledge are combined using a hybrid serial-parallel (HSP) strategy.
Consequently, we achieve a promising accuracy of 97.20% and 96.87% on
CASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best
results previously reported in the literature.
