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摘要: 本文对自然场景文本检测问题及其方法的研究进展进行了综述.首先，论述了自然场景文本的特点、自然场景文本检测技术的研究背景、现状以及主要技术路线.其次，从传统文本检测以及深度学习文本检测的视角出发，梳理、分析并比较了各类自然场景文本检测方法的优缺点，并介绍了端对端文本识别技术.再次，论述了自然场景文本检测技术所面临的挑战，探讨了相应的解决方案.最后，本文列举了测试基准数据集、评估方法，将最具代表性的自然场景文本检测方法的性能进行了比较，本文还展望了本领域的发展趋势.Abstract: In this paper, the research progress of natural scene text detection problems and methods are reviewed. Firstly, the characteristics of natural scene text are introduced, and the text detection technology research background, status and the main technical route are illustrated respectively. Secondly, from the perspective of traditional text detection and deep learning text detection, the merits and demerits of various methods are analyzed, and the technology of end-to-end text recognition is introduced. Then, the challenges of the natural scene text detection technology and the corresponding solutions are discussed. Finally, the benchmark datasets are enumerated, the evaluation methods and the performances of the most representative approaches are fundamentally compared. Furthermore, potential application and development trend in this field are summarized.
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Key words:
- Text detection /
- scene text /
- deep learning /
- handcraft feature /
- connected component analysis
1) 本文责任编委 刘成林 -
图 9 主要的深度学习文本检测路线与一些代表性方法 ((a)文献[137]方法, 该方法采用CNN与ACF提取文本候选区域; (b)文献[130]方法, 该方法对faster RCNN进行改进, 并提出Inception-RPN方式提取文本候选区域; (c)文献[37]方法, 该方法提出了Connectionist text proposal network检测文本候选区域; (d)文献[138]方法, 该方法提出旋转区域候选网络(RRPN); (e)文献[139]方法, 该方法提出了垂直回归建议网络(VRPN); (f)文献[33]方法, 该方法采用Segment linking方式解决多方向排列的文本检测问题; (g)文献[31]方法, 该方法以SSD作为基础框架, 提出了一个端对端训练文本检测器(TextBoxes); (h)文献[15]方法, 该方法创新性提出采用四边形窗口(非矩形)的方式检测任意方向排列的文本; (i)文献[41]方法, 该方法提出采用Text-block全卷积网络获得文本候选区域; (j)文献[140]方法, 该方法采用FCN综合多信息属性来获得文本候选区域; (k)文献[50]方法, 该方法参考了DenseBox的架构, 采用FCN网络检测任意方向排列的文本; (l)文献[141]方法, 该方法采用深度卷积神经网络(DCNN)来学习文本的高级视觉表示+循环神经网络(RNN)处理文本序列.)
Fig. 9 The main deep learning text detection framework and some representative methods ((a) method[137], the CNN and the ACF are integrated to obtain the text region proposal. (b) method[130], the inception-RPN has been proposed in this work. (c) method[37], the connectionist text proposal network has been proposed in this work. (d) method[138], the RRPN has been proposed in this work. (e) method[139], the VRPN has been proposed in this work. (f) method[33], the segment and linking has been proposed in this work. (g) method[31], the TextBoxes method has been proposed in this work. (h) method[15], the deep matching prior network (DMPNet) with tighter quadrangle has been proposed in this work. (i) method[41], the text-block FCN has been proposed in this work. (j) method[140], the FCN and multi-channel prediction method has been proposed in this work. (k) method[50], the DenseBox framework has been followed and the FCN has been proposed in this work. (l) method[141], the DCNN and the RNN has been adopted in this work.)
表 1 常用自然场景文本检测数据集
Table 1 Widely used natural scene text detection datasets and their download link
数据集 年份 数据集大小 图像数目(训练/测试) 文本数目(训练/测试) 文本种类 文本排列方向 ICDAR$'$03[161] 2003 120.2 MB 509 (258/251) 2 276 (1 110/1 156) 英文 水平方向 ICDAR$'$11[30] 2011 266 MB 484 (229/255) 2 037 (848/1 189) 英文 水平方向 ICDAR$'$13[123] 2013 250 MB 462 (229/233) 1 943 (848/1 095) 英文 水平方向 ICDAR$'$15[32] 2015 131.8 MB 1 500 (1 000/500) 17 548 英文 水平方向 SVT[88] 2010 112 MB 350 (100/250) 904 (257/647) 英文 水平方向 MSRA-TD500[39] 2012 96 MB 500 (300/200) 1 719 (1 068/651) 中文/英文 任意方向 KIST[162] 2010 347.4 MB 3 000 $>5 000$ 英文/韩文 水平方向 OSTD[21] 2011 17.34 MB 89 218 英文 任意方向 NEOCR[163] 2011 1.3 GB 659 5 238 英文 任意方向 USTB-SV1K[164] 2015 36.1 MB 1 000 (500/500) 2 955 英文 任意方向 COCO-Text[58, 165] 2016 - 63 686 173 589 多语种 任意方向 RCTW-17[51] 2017 5.4 GB $>12 000$ (8 034/4 000) - 中文 任意方向 SCUT-CTW1500[135] 2017 842 MB $1 500$ (1 000/500) 10 000 英文 含弧形排列的任意方向 
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表 2 近期主流自然场景文本检测方法性能总结(数据都是原文报道的结果, 带(*)标记的数据是引自相关论文)
Table 2 Performance summary of recent dominant natural scene text detection methods (All results are quoted directly from original papers, except for those marked with (*), which are from a recent related paper.)
方法 年份 数据集 精度(P) 召回率(R) 综合评价指标(f) 检测耗时(s) 方法亮点 Lucas[161] 2003 ICDAR'03 0.55 0.46 0.50 8.7 ICDAR'03竞赛冠军 Hinnerk Becker[170] 2005 ICDAR'03 0.62 0.67 0.62 14.4 ICDAR'05竞赛冠军 Yao[39] 2012 ICDAR'03 0.69 0.66 0.67 - 提出MSRA-TD500数据集, 检测任意方向文本 Epshtein[34] 2010 ICDAR'03 0.73 0.60 0.66 - 首次提出笔画宽度变换文本检测方法 SFT-TCD[72] 2013 ICDAR'03 0.81 0.74 0.72 - 提出笔画特征变换 Neumann[60] 2010 ICDAR'03 0.59 0.55 0.57 - 首次提出MSER文本检测方法 Kim[30] 2011 ICDAR'11 0.83 0.63 0.71 - ICDAR'11竞赛冠军 SFT-TCD[72] 2013 ICDAR'11 0.82 0.75 0.73 - 提出笔画特征变换 Yin[164] 2015 ICDAR'11 0.84 0.66 0.74 - 提出自适应聚类文本检测 Zhang[87] 2015 ICDAR'11 0.84 0.76 0.80 - 提出文本行上下结构相似的文本检测 Yin[14] 2014 ICDAR'11 0.86 0.68 0.76 - 提出基于MSER文本检测 Gupta[148] 2016 ICDAR'11 0.92 0.75 0.82 - 首次提出大规模合成场景文本数据集 Liao[31] 2017 ICDAR'11 0.89 0.82 0.86 0.73 提出端对段卷积神经网络 USTB TexStar[123] 2013 ICDAR'13 0.89 0.67 0.76 - ICDAR'13竞赛冠军 Yin[164] 2015 ICDAR'13 0.84 0.65 0.73 - 提出自适应聚类文本检测 Zhang[87] 2015 ICDAR'13 0.88 0.74 0.80 - 提出文本行上下结构相似的文本检测 Zhu[64] 2016 ICDAR'13 0.86 0.74 0.80 - 提出场景上下文检测文本 Zhang[41] 2016 ICDAR'13 0.88 0.78 0.83 - 首次提出基于FCN检测任意方向文本 Gupta[148] 2016 ICDAR'13 0.92 0.76 0.83 - 首次提出大规模合成场景文本数据集 Huang[42] 2016 ICDAR'13 0.88 0.72 0.79 - 提出基于视觉注意的文本检测方法 Liao[31] 2017 ICDAR'13 0.88 0.83 0.85 0.73 提出端对段卷积神经网络 Shi[33] 2017 ICDAR'13 0.88 0.83 0.85 20.6 提出改进版的SSD文本检测器 Stradvision-2[32] 2015 ICDAR'15 0.78 0.37 0.50 - ICDAR'15竞赛冠军 Zhang[41] 2016 ICDAR'15 0.71 0.43 0.54 2.1 首次提出基于FCN检测任意方向文本 Zheng[49] 2017 ICDAR'15 0.62 0.40 0.48 - 提出文本行熵方法 Liu[15] 2017 ICDAR'15 0.73 0.68 0.71 - 提出DMPNet文本检测网络 Shi[33] 2017 ICDAR'15 0.73 0.77 0.75 - 提出改进版的SSD文本检测器 Zhou[50] 2017 ICDAR'15 0.83 0.78 0.81 提出基于FCN与NMS简单高效的文本框架 Yao[39] 2012 MSRA-TD500 0.63 0.63 0.60 7.2 提出MSRA-TD500数据集, 检测任意方向文本 Zhang[41] 2016 MSRA-TD500 0.83 0.67 0.74 - 首次提出基于FCN检测任意方向文本 Huang[42] 2016 MSRA-TD500 0.74 0.68 0.71 - 提出基于视觉注意的文本检测方法 Shivakumara[171] 2017 MSRA-TD500 0.68 0.54 0.60 - 提出基于分形(Fractals)文本检测 Kang[63] 2014 MSRA-TD500 0.71 0.62 0.66 - 提出高阶关联聚类文本检测 Yin[14] 2014 MSRA-TD500 0.71 0.61 0.66 0.8 提出基于MSER文本检测 Yin[164] 2015 MSRA-TD500 0.81 0.63 0.71 1.4 提出自适应聚类文本检测 Zhou[50] 2017 MSRA-TD500 0.87 0.67 0.76 提出基于FCN与NMS简单高效的文本框架 Shi[33] 2017 MSRA-TD500 0.86 0.70 0.77 8.9 提出改进版的SSD文本检测器 Yi[21] 2011 OSTD 0.71 0.62 0.62 17.8 提出组件分析文本检测 Yao[39] 2012 OSTD 0.77 0.73 0.74 - 提出MSRA-TD500数据集, 检测任意方向文本 Yin[164] 2015 USTB-SV1K 0.45$^{*}$ 0.45$^{*}$ 0.45$^{*}$ - 提出基于MSER文本检测 Yao[164] 2015 USTB-SV1K 0.46$^{*}$ 0.44$^{*}$ 0.45$^{*}$ - 提出统一的文本检测与识别框架 Yin[164] 2015 USTB-SV1K 0.50 0.45 0.48 - 提出自适应聚类文本检测 Neumann[59] 2012 SVT 0.19 0.33 0.26 - 提出端对端的文本检测与识别方法 Zhu[64] 2016 SVT 0.41 0.34 0.37 - 提出场景上下文检测文本 Gupta[148] 2016 SVT 0.26 0.27 0.27 - 首次提出大规模合成场景文本数据集 SnooperText[172] 2014 SVT 0.36 0.54 0.43 - 提出自顶向下与自底向上的检测策略 Yin[164] 2015 NEOCR 0.41 0.25 0.31 - 提出自适应聚类文本检测 Yao[104] 2014 COCO-Text 0.3 0.27 0.33 - 提出Strokelet文本区域描述方法 Zhou[50] 2017 COCO-Text 0.50 0.32 0.40 - 提出FCN与NMS简单高效的文本框架 Jin[173] 2011 KAIST 0.85 0.90 - - 提出Touchline文本检测方法 Foo and Bar[51] 2017 RCTW-17 0.74 0.59 0.66 - RCTW-17竞赛冠军 NLPR PAL[51] 2017 RCTW-17 0.77 0.57 0.66 - RCTW-17竞赛亚军
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