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深度学习在手写汉字识别中的应用综述

金连文 钟卓耀 杨钊 杨维信 谢泽澄 孙俊

金连文, 钟卓耀, 杨钊, 杨维信, 谢泽澄, 孙俊. 深度学习在手写汉字识别中的应用综述. 自动化学报, 2016, 42(8): 1125-1141. doi: 10.16383/j.aas.2016.c150725
引用本文: 金连文, 钟卓耀, 杨钊, 杨维信, 谢泽澄, 孙俊. 深度学习在手写汉字识别中的应用综述. 自动化学报, 2016, 42(8): 1125-1141. doi: 10.16383/j.aas.2016.c150725
JIN Lian-Wen, ZHONG Zhuo-Yao, YANG Zhao, YANG Wei-Xin, XIE Ze-Cheng, SUN Jun. Applications of Deep Learning for Handwritten Chinese Character Recognition: A Review. ACTA AUTOMATICA SINICA, 2016, 42(8): 1125-1141. doi: 10.16383/j.aas.2016.c150725
Citation: JIN Lian-Wen, ZHONG Zhuo-Yao, YANG Zhao, YANG Wei-Xin, XIE Ze-Cheng, SUN Jun. Applications of Deep Learning for Handwritten Chinese Character Recognition: A Review. ACTA AUTOMATICA SINICA, 2016, 42(8): 1125-1141. doi: 10.16383/j.aas.2016.c150725

深度学习在手写汉字识别中的应用综述

doi: 10.16383/j.aas.2016.c150725
基金项目: 

国家自然科学基金 61472144

广东省科技计划 2014A010103012, 2015B010101004, 2015B010130003, 2015B010131004

详细信息
    作者简介:

    钟卓耀 华南理工大学电子与信息学院博士研究生. 2015 年获得华南理工大学工学学士学位. 主要研究方向为机器学习, 模式识别, 自然场景文字检测与识别. E-mail: z.zhuoyao@mail.scut.edu.cn

    杨钊 广州大学机械与电气工程学院讲师. 2014 年获得华南理工大学信息与通信工程专业博士学位. 主要研究方向为机器学习, 模式识别, 计算机视觉. E-mail: 15989023646@126.com

    杨维信 华南理工大学电子与信息学院博士研究生. 2013 年获得华南理工大学工学学士学位. 主要研究方向为机器学习, 手写分析和识别, 计算机视觉和智能系统. E-mail: wxy1290@163.com

    谢泽澄 华南理工大学电子与信息学院博士研究生. 2014 年获得华南理工大学工学学士学位. 主要研究方向为机器学习, 文档分析与识别, 计算机视觉和人机交互. E-mail: xiezcheng@foxmail.com

    孙俊 富士通研究开发中心有限公司信息技术研究部部长. 2002 年获得清华大学模式识别和智能系统博士学位. 主要研究方向为图像处理、机器学习和模式识别. E-mail: sunjun@cn.fujitsu.com

    通讯作者:

    金连文 华南理工大学电子与信息学院教授. 1991 年在中国科学技术大学获得学士学位, 1996 年在华南理工大学获得博士学位. 主要研究方向为模式识别, 深度学习, 文字识别, 图像处理, 计算机视觉. E-mail: lianwen.jin@gmail.com

  • 中图分类号: 

Applications of Deep Learning for Handwritten Chinese Character Recognition: A Review

Funds: 

National Natural Science Foundation of China 61472144

Guangdong Science and Technology Plan 2014A010103012, 2015B010101004, 2015B010130003, 2015B010131004

More Information
    Author Bio:

    ZHONG Zhuo-Yao Ph. D. candi- date at the School of Electronic and Information Engineering, South China University of Technology. He received his B. S. degree in electronics and information engineering from South China University of Technology in 2015. His research interest cov- ers machine learning, pattern recognition, text detection and recognition in natural scen

    YANG Zhao Lecturer at the School of Mechanical and Electric Engi- neering, Guangzhou University. He re- ceived his Ph. D. degree from South China University of Technology in 2014. His research interest covers machine learning, pattern recognition, and computer visio

    YANG Wei-Xin Ph. D. candidate at the School of Electronic and Infor- mation Engineering, South China Uni- versity of Technology. He received his B. S. degree in elec- tronics and information engineering from South China Uni- versity of Technology in 2013. His research interest cov- ers machine learning, handwriting analysis and recognition, computer vision, and intelligent system

    XIE Ze-Cheng Ph. D. candidate at the School of Electronic and Infor- mation Engineering, South China Uni- versity of Technology. He received his B. S. degree in elec- tronics and information engineering from South China Uni- versity of Technology in 2014. His research interest covers machine learning, document analysis and recognition, com- puter vision, and human-computer interactio

    SUN Jun Director of Informa- tion Technology Laboratory, Fujitsu Research & Development Center Co., Ltd. He received his Ph. D. degree in pattern recogni- tion and intelligent system from Tsinghua University in 2002. His research interest covers image processing, ma- chine learning, and pattern recognitio

    Corresponding author: JIN Lian-Wen Professor at the School of Electronic and Information Engineering, South China University of Technology. He obtained his B. S. de- gree from the Department of Electronics Engineering, Uni- versity of Science and Technology of China and Ph. D. de- gree in communication and information system from South China University of Technology in 1991 and 1996, respec- tively. His research interest covers pattern recognition, deep learning, character recognition, image processing and computer vision.
  • 摘要: 手写汉字识别(Handwritten Chinese character recognition,HCCR)是模式识别的一个重要研究领域,最近几十年来得到了广泛的研究与关注,随着深度学习新技术的出现,近年来基于深度学习的手写汉字识别在方法和性能上得到了突破性的进展.本文综述了深度学习在手写汉字识别领域的研究进展及具体应用.首先介绍了手写汉字识别的研究背景与现状.其次简要概述了深度学习的几种典型结构模型并介绍了一些主流的开源工具,在此基础上详细综述了基于深度学习的联机和脱机手写汉字识别的方法,阐述了相关方法的原理、技术细节、性能指标等现状情况,最后进行了分析与总结,指出了手写汉字识别领域仍需要解决的问题及未来的研究方向.
  • 图  1  几种常用的手写汉字数据增广技术示意图

    Fig.  1  The influences of the controller parameters on the tracking errors

    图  2  手写汉字的路径积分特征图可视化

    Fig.  2  Path signature feature map visualization of handwritten Chinese characters

    表  1  目前一些主流的深度学习开源仿真工具及其下载地址

    Table  1  Some mainstream deep-learning open source toolboxes and their download address at present

    工具名称说明及备注下载地址
    Caffe[112] UC Berkeley BVLC 实验室发布的深度学习开源工具,是目前使用最为广泛的深度学习实验平台之一https://github.com/BVLC/caffe
    Theano[113-114] 基于Python 语言的深度学习开源仿真工具https://github.com/Theano/Theano
    Torch[115] 基于Lua 脚本语言的工具,支持iOS、Android 等嵌入式平台http://torch.ch/
    Purine[116] 支持多GPU,提供线性加速能力https://github.com/purine/purine2
    MXNet[117] 由百度牵头组织的深度机器学习联盟(DMCL) 发布的C++ 深度学习工具库https://github.com/dmlc/mxnet
    DIGITS[118] 由NVIDIA 公司集成开发发布的一款基于Web 页面的可视化深度学习仿真工具,支持Caffe 及Touch 工程代码https://github.com/NVIDIA/DIGITS
    ConvNet[119] 最早的支持GPU 的CNN 开源工具之一,ILSVRC2012 比赛第一名提供的代码https://code.google.com/p/cuda-convnet/
    Cuda-ConvNet2[109] 支持多GPU 的ConvNet https://github.com/akrizhevsky/cuda-convnet2
    DeepCNet[120]英国Warwick 大学Graham 教授发布的开源CNN 仿真工具,曾获ICDAR 2013 联机手写汉字识别竞赛第一名https://github.com/btgraham/SparseConvNet
    Petuum[121]CMU 发布的一款基于多CPU/GPU 集群并行化分布式,机器学习开源仿真平台除了支持深度学习的常用算法之外,还提供很多传统机器学习算法的实现. 可部署在云计算平台之中https://github.com/petuum/bosen/wiki
    CURRENT[122] 支持GPU 的回归神经网络函数库http://sourceforge.net/projects/currennt/
    Minerva[123] 深度机器学习联盟(DMCL) 发布的支持多GPU 并行化的深度学习工具https://github.com/dmlc/minerva
    TensorFlow[124] 谷歌发布的机器学习可视化开发工具,支持多CPU 及多GPU 并行化仿真,支持CNN、RNN 等深度学习模型https://github.com/tensor°ow/tensor°ow
    DMTK[125] 微软发布的一套通用的分布式深度学习开源仿真工具https://github.com/Microsoft/DMTK
    下载: 导出CSV

    表  2  不同方法在CASIA-OLHWDB1.1联机手写中文单字数据集上的识别结果对比

    Table  2  Comparison with different methods on the CASIA-OLHWDB1.1

    方法准确率 (%) 伪样本变形 模型集成 (模型数量)
    传统最佳方法: DFE+DLQDF[10] 94.85 × ×
    HDNN-SSM-MCE[66] 89.39 × ×
    MCDNN[127] 94.39 √(35)
    DeepCNet[40] 96.42 ×
    DeepCNet-8方向直方图特征[40] 96.18 ×
    DCNN (4种领域知识融合)[60] 96.35 ×
    HSP-DCNN (4种领域知识集成)[64] 96.87 √(8)
    DeepCNet-FMP (单次测试)[132] 96.74 ×
    DeepCNet-FMP (多次测试)[132] 97.03 √(12 test)
    DropSample-DCNN[61] 96.55 ×
    DropSample-DCNN (集成)[61] 97.06 √(9)
    下载: 导出CSV

    表  3  不同深度学习方法在CASIA-OLHWDB1.0-1.1以及ICDAR2013竞赛数据集上的识别结果 (%)

    Table  3  Comparison with different methods on the CASIA-OLHWDB1.0-1.1 and ICDAR 2013 Online CompetitionDB (%)

    CASIA- OLHWDB1.0 CASIA- OLHWDB1.1 ICDAR 2013 竞赛数据集
    MQDF传统方法[10] 95.2894.85 92.62
    MCDNN[127] 94.39 - -
    DeepCNet[40] - 96.42 97.391
    DropSample-
    DCNN[61] 96.9396.55 97.231
    DropSample-
    DCNN (集成)[61] 97.3397.06 97.51
    1 DeepCNet模型参数大约为590万个,DropSample-DCNN的模型参数为380万个.
    下载: 导出CSV

    表  4  不同深度学习方法及部分典型的传统方法在ICDAR2013脱机手写汉字竞赛集上的识别性能

    Table  4  Comparison with different traditional and deep-learning besed methods on ICDAR 2013 Offline CompetitionDB

    方法 Top1 (%) Top5 (%) Top10 (%) 模型存储量
    HCCR-Gradient-GoogLeNet[77] 96.28 99.56 99.80 27.77MB
    HCCR-Gabor-GoogLeNet[77] 96.3599.6 99.80 27.77MB
    HCCR-Ensemble-GoogLeNet[77] (average of 4 models) 96.6499.64 99.83 110.91MB
    HCCR-Ensemble-GoogLeNet[77] (average of 10 models) 96.7499.65 99.83 277.25MB
    CNN-Fujitsu[39] 94.77 - 99.59 2460MB
    MCDNN-INSIA[74] 95.79 - 99.54 349MB
    MQDF-HIT[39] 92.61 - 98.99 120MB
    MQDF-THU[39] 92.56 - 99.13 198MB
    DLQDF[39] 92.72 - - -
    ART-CNN[76] 95.04 - - 51.64MB2
    R-CNN Voting[76] 95.55 - - 51.64MB2
    ATR-CNN Voting[76] 96.06 - - 206.56MB2
    MQDF-CNN[78] 94.44 - - -
    Multi-CNN Voting[129] 96.79 - - -
    2根据文献[76]给出的模型参数(CNN层数、各层卷积核大小及数量、聚合层大小及数量、全连接数量),按照每个参数以浮点数存储(占用4个字节)方式推算而得.
    下载: 导出CSV

    表  5  不同研究方法在ICDAR 2013 Offine Text CompetitionDB 数据对比记录表(%)

    Table  5  Comparison with di®erent methods on the ICDAR 2013 Offine Text CompetitionDB (%)

    方法/系统名称 CR AR CER
    HIT-MQDF+LM[39] 88.8 86.7 13.3
    THU-MQDF+DP[39] 86.183.6 6.4
    MQDF+Multiple Contexts[35] 89.390.2 10.7
    MDLSTM-RNN[138] - 83.5 16.5
    MDLSTM-RNN+LM 4-gram[138] - 90.4 10.6
    下载: 导出CSV
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  • 收稿日期:  2015-11-04
  • 录用日期:  2016-04-18
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