  This paper presents an investigation of several techniques that increase the
accuracy of online handwritten Chinese character recognition (HCCR). We propose
a new training strategy named DropDistortion to train a deep convolutional
neural network (DCNN) with distorted samples. DropDistortion gradually lowers
the degree of character distortion during training, which allows the DCNN to
better generalize. Path signature is used to extract effective features for
online characters. Further improvement is achieved by employing spatial
stochastic max-pooling as a method of feature map distortion and model
averaging. Experiments were carried out on three publicly available datasets,
namely CASIA-OLHWDB 1.0, CASIA-OLHWDB 1.1, and the ICDAR2013 online HCCR
competition dataset. The proposed techniques yield state-of-the-art recognition
accuracies of 97.67%, 97.30%, and 97.99%, respectively.
