Applications of deep learning for handwritten chinese character recognition: a review

被引：0

作者：

Jin L.-W. ^{[1
]}

Zhong Z.-Y. ^{[1
]}

Yang Z. ^{[2
]}

Yang W.-X. ^{[1
]}

Xie Z.-C. ^{[1
]}

Sun J. ^{[3
]}

机构：

[1] School of Electronic and Information Engineering, South China University of Technology, Guangzhou

[2] School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou

[3] Information Technology Laboratory, Fujitsu Research & Development Center Co., Ltd, Beijing

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2016年 / 42卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Convolutional neural network; Deep learning; Handwritten Chinese character recognition (HCCR); Long-short term memory (LSTM); Recurrent neural network; Stacked auto-encoder;

D O I：

10.16383/j.aas.2016.c150725

中图分类号：

学科分类号：

摘要：

Handwritten Chinese character recognition (HCCR) is an important research filed of pattern recognition, which has attracted extensive studies during the past decades. With the emergence of deep learning, new breakthrough progresses of HCCR have been obtained in recent years. In this paper, we review the applications of deep learning models in the field of HCCR. First, the research background and current state-of-the-art HCCR technologies are introduced. Then, we provide a brief overview of several typical deep learning models, and introduce some widely used open source tools for deep learning. The approaches of online HCCR and offline HCCR based on deep learning are surveyed, with the summaries of the related methods, technical details, and performance analysis. Finally, further research directions are discussed. Copyright © 2016 Acta Automatica Sinica. All rights reserved.

引用

页码：1125 / 1141

页数：16

共 149 条

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