A sophisticated offline network developed for recognizing handwritten Chinese character efficiently

Abstract

In this paper, we designed an efficient network structure by combining multiple scale convolution shuffle (MSCS) module and attention features spatial aggregation (ASA) module. MSCS module can obtain local and global multiple receptive fields feature information, and it contains the structure idea of the lightweight network convolution block. Moreover, we have proposed ASA module to reducing the information loss of different features in spatial dimension reduction. Meanwhile, we introduce the joint loss function to strengthen the ability of the model to distinguish the difference of attention features between inter-class and intra-class. The experimental results tested on offline HCCR competition dataset ICDAR-2013 show that our network model trained only on handwritten dataset only takes 3.97 ms to recognize a character image and achieves 97.63% accuracy just requires 22.9 MB for storage. Therefore, our method reaches the state-of-the-art in efficient single networks particularly in view of the storage space and inference time.