  In this paper, we propose a novel model, named Stroke Sequence-dependent Deep
Convolutional Neural Network (SSDCNN), using the stroke sequence information
and eight-directional features for Online Handwritten Chinese Character
Recognition (OLHCCR). On one hand, SSDCNN can learn the representation of
Online Handwritten Chinese Character (OLHCC) by incorporating the natural
sequence information of the strokes. On the other hand, SSDCNN can incorporate
eight-directional features in a natural way. In order to train SSDCNN, we
divide the process of training into two stages: 1) The training data is used to
pre-train the whole architecture until the performance tends to converge. 2)
Fully-connected neural network which is used to combine the stroke
sequence-dependent representation with eight-directional features and softmax
layer are further trained. Experiments were conducted on the OLHCCR competition
tasks of ICDAR 2013. Results show that, SSDCNN can reduce the recognition error
by 50\% (5.13\% vs 2.56\%) compared to the model which only use
eight-directional features. The proposed SSDCNN achieves 97.44\% accuracy which
reduces the recognition error by about 1.9\% compared with the best submitted
system on ICDAR2013 competition. These results indicate that SSDCNN can exploit
the stroke sequence information to learn high-quality representation of OLHCC.
It also shows that the learnt representation and the classical
eight-directional features complement each other within the SSDCNN
architecture.
