Applications of Deep Learning for Handwritten Chinese Character Recognition: A Review
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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.Abstract: 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.
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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)
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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 (%)
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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] 96.35 99.6 99.80 27.77MB HCCR-Ensemble-GoogLeNet[77] (average of 4 models) 96.64 99.64 99.83 110.91MB HCCR-Ensemble-GoogLeNet[77] (average of 10 models) 96.74 99.65 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.
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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 (%)
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