Survey on scene text detection based on deep learning
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摘要: 在大数据驱动应用的背景下，随着计算机硬件性能的提高，基于深度学习的目标检测和图像分割算法冲破了传统算法的瓶颈，成为当前计算机视觉领域的主流算法.而场景文字检测任务受到目标检测和图像分割算法发展的影响，近年来也有了极大的突破.这篇综述的目的主要有3个方面：介绍近5年场景文字检测工作进展；比较分析先进算法的优点及不足；总结该领域相关的基准数据集和评价方法.Abstract: With improvements in computer hardware performance, object detection, and image segmentation algorithms (based on deep learning) have broken the bottlenecks posed by traditional algorithms in big data-driven applications and become the mainstream algorithms in the field of computer vision. In this context, scene text detection algorithms have made great breakthroughs in recent years. The objectives of this survey are three-fold:introduce the progress of scene text detection over the past 5 years, compare and analyze the advantages and limitations of advanced algorithms, and summarize the relevant benchmark datasets and evaluation methods in the field.
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Key words:
- text detection /
- deep learning /
- natural scene /
- object detection /
- image segmentation
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图 3 基于图像分割的场景文字检测算法示例
Fig. 3 Examples of scene text detection algorithms based on image segmentation
图 4 ICDAR 2015-Incidental场景文字数据集示例
Fig. 4 Examples from the ICDAR 2015 incidental scene text dataset
表 1 近几年场景文字检测算法的优势及局限
Tab. 1 Advantages and limitations of scene text detection algorithms in recent years
方法 算法 优势 局限 基于目
标检测
的方法Tian等人(CTPN)[29] 速度快, 性能佳 只能处理水平文字 Zhong等人(DeepText)[30] 速度快, 性能佳, 合成数据, 训练样本少 只能处理水平文字 Zhang等人(FEN)[33] 速度快, 性能佳 只能处理水平文字 Jiang等人(R2CNN)[31] 多方向 速度慢 Ma等人(RRPN)[32] 多方向 速度慢 Shi等人(SegLink)[36] 多方向, 长文字, 速度快 不能检测间隔大的文字行;
不能检测形变或弯曲的文字Tian等人(WeText)[37] 速度快, 训练数据集扩增 只能处理水平文字 基于图
像分割
的方法Zhang等人(Text-Block FCN)[40] 多方向, 多语言, 多字体 速度慢 He等人(CCTN)[41] 多方向, 多尺寸, 鲁棒 速度慢 Yao等人(HED-based)[42] 多方向, 多语言, 弯曲文字 速度慢, 不适用于模糊
严重和高亮文字Polzounov等人(Wordfence)[43] 多方向, 多语言, 多尺寸 速度慢 Deng等人(PixelLink)[44] 多方向, 性能佳, 鲁棒 对弯曲文字检测不好 Yang等人(IncepText)[45] 多方向, 性能佳, 已在OCR产品中实现 速度慢 混合
方法Dai等人(FTSN)[46] 多方向, 弯曲文字, 性能佳 速度慢 He等人(DDRN)[47] 多方向, 直接, 高效, 一步后处理 对间隔很大、单个字符、
复杂背景的文字行检测不好Jiang等人(CCP)[48] 多方向, 多语言 速度慢 Zhou等人(EAST)[49] 多方向, 速度快, 高效 对较长文字检测效果不好 Qin和Manduchi(CSDN)[38] 多方向, 不需要后处理 对弯曲文字检测不好 
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表 2 近几年端到端文字检测系统的优势与局限
Tab. 2 Advantages and limitations of end-to-end text detection systems in recent years
算法 年份 优势 局限 Wang等人[6] 2012 第一个将深度学习用于场景文字检测系统中, 性能好, 鲁棒 只能处理水平文字 Jaderberg等人[5] 2014 鲁棒, 适用于不同分辨率的图像 只能处理水平文字, 要求大量训练数据, 低效 Jaderberg等人[51] 2016 合成场景文字图像, 高召回率 只适用于给定语言, 只能处理水平文字 Gupta等人(FCRN)[52] 2016 合成场景文字图像, 速度快 只能处理水平文字 Liao等人(TextBoxes)[53] 2017 速度快, 准确率高, 支持多
尺度输入, 一步后处理只能处理水平文字, 对间隔大的字符
和垂直文字检测不佳Li等人[54] 2017 第一个尝试将场景文字检测
和识别集成到一个只能处理水平文字, 对小文字识别效果不佳
单一网络中的模型, 可以处理多尺度文字Busta等人(Deep TextSpotter)[55] 2017 速度快, 性能佳, 可以
处理多方向文字对单个字符或者简短的数字和
字符片段检测不佳Liao等人(TextBoxes++)[56] 2018 鲁棒, 速度快, 可以处理
多方向文字对间隔大的字符和垂直
文字检测不佳Bartz等人(SEE)[57] 2018 网络架构简单, 网络可以
自动学习文字检测只能处理水平文字, 训练困难 Liu等人(FOTS)[58] 2018 鲁棒, 速度快, 可以处理多
方向文字及长文字对文字区域内存在较大方差或者
文字区域和背景有相似模式时不适用
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表 3 场景文字检测常用数据集
Tab. 3 Common datasets for scene text detection
数据集年份 图片数量(训练, 测试) 方向(弯曲) 语言 ICDAR 2003[63]/ ICDAR 2005[64] 509(258, 251) 水平 英文 ICDAR 2011[65] 484(229, 255) 水平 英文 ICDAR 2013[66]/ICDAR 2015-Focused[67] 462(229, 233) 水平 英文 ICDAR 2015-Incidental[67] 1500(1000, 500) 多方向 英文 ICDAR 2017-MLT[68] 9000(7200, 1800) 多方向 多语言 KAIST 2010[69] 3000 水平 英韩文 SVT 2010[3] 350(100, 250) 水平 英文 NEOCR 2011[70] 659 多方向(弯曲) 多语言 OSTD 2011[71] 89 多方向 英文 MSRA-TD500 2012[9] 500(300, 200) 多方向 中英文 CUTE80 2014[72] 80 弯曲 英文 HUST-TR400 2014[73] 400 多方向 英文 USTB-SV1K 2015[74] 1000(500, 500) 多方向 英文 SCUT-FORU-DB 2016[75] 3931 水平 中英文 COCO-Text 2016[76] 63686(43686, 20000) 多方向 英文 RCTW-17 2017[77] 12263(8034, 4229) 多方向 中文 Total-Text 2017[78] 1555(1255, 300) 多方向(弯曲) 英文 CTW1500 2017[79] 1500(1000, 500) 多方向(弯曲) 中英文 CTW 2018[80] 32285 多方向(弯曲) 中文
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表 4 场景文字检测算法性能比较
Tab. 4 Performance comparison of scene text detection algorithms
算法 ICDAR2013 ICDAR2015Incidental MSRA-TD500 P/% R/% F/% P/% R/% F/% P/% R/% F/% Tian等人(CTPN)[29] 93.00 83.00 87.70 74.22 51.56 60.85 / / / Zhong等人(DeepText)[30] 87.17 82.79 84.93 / / / / / / Zhang等人(FEN)[33] 89.30 94.10 91.60 / / / / / / Jiang等人(R2CNN)[31] 93.55 82.59 87.73 85.62 79.63 82.54 / / / Ma等人(RRPN)[32] 90.22 71.89 80.02 73.23 82.17 77.44 82.00 68.00 74.00 Shi等人(SegLink)[36] 87.70 83.00 85.30 73.10 76.80 75.00 86.00 70.00 77.00 Tian等人(WeText)[37] 84.20 80.70 82.30 / / / / / / Zhang等人(Text-Block FCN)[40] 88.14 74.00 80.00 70.81 43.09 53.58 83.00 67.00 74.00 He等人(CCTN)[41] 90.00 83.00 86.00 / / / 79.00 65.00 71.00 Yao等人(HED-based)[42] 89.00 80.00 84.00 72.00 59.00 65.00 63.00 62.00 60.00 Polzounov等人(Wordfence)[43] 65.00 92.00 76.00 / / / / / / Deng等人(PixelLink)[44] 87.50 88.60 88.10 85.50 82.00 83.70 83.00 73.20 77.80 Yang等人(IncepText)[45] / / / 93.80 87.30 90.50 87.50 79.00 83.00 Dai等人(FTSN)[46] / / / 88.60 80.00 84.10 87.60 77.10 82.00 He等人(DDRN)[47] 92.00 81.00 86.00 82.00 80.00 81.00 77.00 70.00 74.00 Jiang等人(CCP)[48] 92.20 91.50 91.90 / / / / / / Zhou等人(EAST)[49] / / / 83.27 78.33 80.72 87.30 67.40 76.10 Qin等人(CSDN)[38] 90.00 83.00 86.00 79.00 65.00 71.00 / / / 注: "$P$"、"$R$"、"$F$"分别代表准确率、召回率和$F$度量.
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