自然场景文本检测技术研究综述

白志程; 李擎; 陈鹏; 郭立晴

doi:10.13374/j.issn2095-9389.2020.03.24.002

自然场景文本检测技术研究综述

doi: 10.13374/j.issn2095-9389.2020.03.24.002

白志程^{1, 2},
李擎^{1, 2, ,},
陈鹏³,
郭立晴¹

1.
北京科技大学自动化学院，北京 100083
2.
工业过程知识自动化教育部重点实验室，北京 100083
3.
中国邮政储蓄银行金融科技创新部，北京 100808

基金项目: 国家自然科学基金资助项目（11296089）

详细信息

通讯作者:
E-mail：liqing@ies.ustb.edu.cn

中图分类号: TP18
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-24
- 刊出日期: 2020-11-25

Text detection in natural scenes: a literature review

BAI Zhi-cheng^{1, 2},
LI Qing^{1, 2
, ,},
CHEN Peng³,
GUO Li-qing¹

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
3.
FINTECH Innovation Division, Postal Savings Bank of China, Beijing 100808, China

More Information

Corresponding author: E-mail: liqing@ies.ustb.edu.cn

摘要

摘要: 文本检测在自动驾驶和跨模态图像检索中具有极为广泛的应用。该技术也是基于光学字符的文本识别任务中重要的前置环节。目前，复杂场景下的文本检测仍极具挑战性。本文对自然场景文本检测进行综述，回顾了针对该问题的主要技术和相关研究进展，并对研究现状进行分析。首先对问题进行概述，分析了自然场景中文本检测的主要特点；接着，介绍了经典的基于连通域分析、基于滑动检测窗的自然场景文本检测技术；在此基础上，综述了近年来较为常用的深度学习文本检测技术；最后，对自然场景文本检测未来可能的研究方向进行展望。
- 文本检测 /
- 场景文本 /
- 连通域分析 /
- 图像处理 /
- 统计学习 /
- 深度学习
Abstract: Text detection is widely applied in the automatic driving and cross-modal image retrieval fields. This technique is also an important pre-procedure in optical character-based text recognition tasks. At present, text detection in complex natural scenes remains a challenging topic. Because text distribution and orientation are varied in different scenes and domains, there is still room for improvement in existing computer vision-based text detection methods. To complicate matters, natural scene texts, such as those in guideposts and shop signs, always contain words in different languages. Even characters are missing from some natural scene texts. These circumstances present more difficulties for feature extraction and feature description, thereby weakening the detectability of existing computer vision and image processing methods. In this context, text detection applications in natural scenes were summarized in this paper, the classical and newly presented techniques were reviewed, and the research progress and status were analyzed. First, the definitions of natural scene text detection and associated concepts were provided based on an analysis of the main characteristics of this problem. In addition, the classic natural scene text detection technologies, such as connected component analysis-based methods and sliding detection window-based methods, were introduced comprehensively. These methods were also compared and discussed. Furthermore, common deep learning models for scene text detection of the past decade were also reviewed. We divided these models into two main categories: region proposal-based models and segmentation-based models. Accordingly, the typical detection and semantic segmentation frameworks, including Faster R-CNN, SSD, Mask R-CNN, FCN, and FCIS, were integrated in the deep learning methods reviewed in this section. Moreover, hybrid algorithms that use region proposal ideas and segmentation strategies were also analyzed. As a supplement, several end-to-end text recognition strategies that can automatically identify characters in natural scenes were elucidated. Finally, possible research directions and prospects in this field were analyzed and discussed.
- text detection /
- scene text /
- connected domain analysis /
- image processing /
- statistical learning /
- deep learning

HTML全文

图 1 自然场景示例图片

Figure 1. Sample images of nature scenes

下载: 全尺寸图片幻灯片

图 2 笔划宽度的定义^[13]。（a）一种典型的笔划；（b）笔划边界像素；（c）笔划束上的每个像素

Figure 2. Definition of the stroke width^[13]: (a) a typical stroke; (b) a pixel on the boundary of the stroke; (c) each pixel along the ray

下载: 全尺寸图片幻灯片

图 3 多边形滑动窗口和矩形滑动窗口检测结果比较^[25]。（a）多边形滑窗检测结果；（b）矩形滑窗检测结果

Figure 3. Comparison of the detection results between polygon sliding windows and rectangular sliding windows^[25]: (a) detection results of polygon sliding window; (b) detection result of rectangular sliding window

下载: 全尺寸图片幻灯片

图 4 Text Snake表征图示^[54]

Figure 4. Illustration of the proposed Text Snake representation^[54]

下载: 全尺寸图片幻灯片

图 5 PixelLink结构图^[56]

Figure 5. Architecture of PixelLink^[56]

下载: 全尺寸图片幻灯片

表 1 文本检测常用数据集

Table 1. Common datasets for text detection

Dataset	Presenter	Type	Sample size（Training/Test）	Language	Direction
CTW	THU, Tencent	Scene	32285	Chinese	Horizontal
ICDAR2003	ICDAR	Scene	2276（1110/115）	English	Horizontal
ICDAR2011		Scene	484（229/255）	English	Horizontal
ICDAR2011		Graph	522（420/102）	English	Curve
ICDAR2013		Scene	463（229/233）	English	Horizontal
		Graph	551（410/141）	English	Multiple
		Video	28（13/15）	English, French, Spanish	Multiple
MSRA-TD500	HUST	Scene	500（300/200）	English Chinese	Multiple
COCO-Text	Microsoft	Scene	63686	English	Multiple
RCTW-17	HUST	Scene	12263（8034/4229）	Chinese	Horizontal
RCTW-17	HUST	Scene	12263（8034/4229）	English	Horizontal
MLT2017	ICDAR	Scene	18000（7200/10800）	Multi-lingual	Horizontal
MLT2019	ICDAR	Scene	20000（10000/10000）	Multi-lingual	Horizontal
Total-Text	UM	Scene	1525(1225/300)	English	Multiple
SCUT-CTW1500	SCUT	Scene	1500（1000/500）	Multi-lingual	Multiple
ArT	UM, SCUT, Baidu	Scene	10166(5603/4563)	English	Multiple
ArT	UM, SCUT, Baidu	Scene	10166(5603/4563)	Chinese	Multiple

下载: 导出CSV

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