Handwritten Chinese Characters Recognition Using Two-Stage Hierarchical Convolutional Neural Network
Nina Aleskerova, Aleksei Zhuravlev · 2020
Convolutional Neural Networks (CNNs) are widely used for handwritten character recognition tasks. The task of handwritten Chinese character recognition is especially outlined by a large number of target classes. The total number of Chinese characters exceeds 40,000, the average Chinese uses several thousand characters in his speech. In this article, we will work with about 4,000 Chinese characters. Dealing with a large number of classes might be problematic for a single-network approach in terms of both speed and accuracy, especially when running on a CPU. In this paper, we suggest an approach to meet this challenge to do separate handwritten Chinese characters recognition. We propose that a hierarchy of multiple neural networks should be used wherein a first-level network chooses a second-level network that performs the final recognition. Further, as every second-level network is only trained for a subset of classes, it can be simpler and more accurate than the whole classification network. In our approach, we combine several classes of Chinese characters with similar external features in one group (cluster) and work with these clusters further. Experimental results on CASIA 200-class and 3755-class handwritten datasets are presented, which compare the proposed hierarchical approach with the classical single-network approach.