Compressing the CNN architecture for in-air handwritten Chinese character recognition

Abstract

Since the convolutional neural network (CNN) has brought great breakthroughs in the field of computer vision, it recently has been introduced to the in-air handwritten Chinese character recognition (IAHCCR) to achieve better recognition performance. However, the CNN is typically over-parameterized and contains lots of redundant filters or parameters. This leads the CNN to suffer from huge computation cost and considerable storage usage, limiting its deployments to resource-constrained devices like mobile phones and intelligent TVs. In this paper, we propose a unified algorithm to effectively compress the CNN for IAHCCR with little accuracy loss. Specifically, we first utilize the channel pruning strategy to simplify the network structure, and then adopt the network quantization technique to represent parameters with lower precision. We conduct experiments on the in-air handwriting dataset IAHCC-UCAS2016, where the baseline CNN achieves the state-of-the-art accuracy of 95.33% with 15.5 MB of storage. After the compression, we achieve 12.4 × storage saving and 1.7 × theoretical acceleration with only 0.17% accuracy loss. Moreover, evaluations on other benchmark datasets including ICDAR-2013 and MNIST further demonstrate the effectiveness of the proposed method.