A Natural Scene Text Extraction Method Based on the Maximum Stable Extremal Region and Stroke Width Transform

doi:10.7652/xjtuxb201701021

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A Natural Scene Text Extraction Method Based on the Maximum Stable Extremal Region and Stroke Width Transform

更新时间：2025-07-09

- A Natural Scene Text Extraction Method Based on the Maximum Stable Extremal Region and Stroke Width Transform
- Vol. 51, Issue 1, Pages: 135-140(2017)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201701021
  CLC： TP391.1
- Online First：10 January 2017，
  
  Published：2017
- 稿件说明：
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A Natural Scene Text Extraction Method Based on the Maximum Stable Extremal Region and Stroke Width Transform[J]. 2017, 51(1): 135-140.
DOI：

A Natural Scene Text Extraction Method Based on the Maximum Stable Extremal Region and Stroke Width Transform[J]. 2017, 51(1): 135-140. DOI： 10.7652/xjtuxb201701021.

摘要

针对从背景复杂、视角多变、语言形式多样的场景图像中难以准确提取文本信息的问题

提出了一种基于最大稳定极值区域(MSER)和笔画宽度变换(SWT)场景文本提取方法。该方法结合MSER、SWT算法的优点

采用MSER算法的准确检测文字区域

建立文本候选区域

利用SWT算法计算文本候选区域笔画宽度得到候选文本区域的笔画宽度; 根据笔画宽度图

利用连通域标记建立笔画宽度连通图

然后根据笔画宽度连通图

建立笔画连通图的启发性规则

删除非文本候选区域

并根据文本的几何特征分析及局部自适应窗口最大类间方差(Otsu)分割

有效提取出自然场景图像中的文本

文本提取的准确率、召回率及综合性能分别为0.74、0.64及0.68。仿真实验结果表明

在文本视角多变

字符大小、尺寸、字体各异的复杂条件下

所提方法具有较好的鲁棒性

适用于多语言和多字体混合的场景文本提取。

Abstract

To extract text information effectively from natural scene image with complex background

multi-orientation perspective and multilingual languages

a scenario text extraction method based on maximum stable extremal region(MSER)and stroke width transform(SWT)is presented. The method combines the merits of MSER and SWT algorithms. It establishes text candidate regions by utilizing MSER algorithm to detect text regions

and SWT algorithm is used to calculate the text stroke width of candidate region to get its stroke width. According to the stroke width graph

the stroke-connected graph is established by using connected component labeling. Then the heuristic rules of stroke-connected graph are established to remove non-text candidate regions according to the stroke-connected graph. By using the geometrical feature analysis and the local adaptive window Otsu segmentation

the text in natural scene images can be extracted effectively. The text extraction accuracy

recall rate and comprehensive performance of this method are 0.74

0.64 and 0.68

respectively. Simulation experiment shows that the method can achieve good robustness for complex background with multi-orientation perspective

various characters and font sizes

and it is suitable for variety of languages and fonts.

关键词

Keywords

references

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