A Novel Approach to Text Detection and Extraction from Videos by Discriminative Features and Density

Text is very important to video retrieval, index, and understanding. However, its detection and ex- traction is challenging due to varying background~ low con- trast between text and non-text regions, and perspective distortion. In this paper, we propose a novel two phase approach to tackling this p...

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Bibliographic Details
Published in:电子学报：英文版 2014-04, Vol.23 (2), p.322-328
Main Author: WEI Baogang ZHANG Yin YUAN Jie LIU Yonghuai WANG Lidong
Format: Article
Language:English
Subjects:
低对比度
判别
提取
支持向量机
文本检测
视频检索
边缘像素
边缘密度
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Summary:Text is very important to video retrieval, index, and understanding. However, its detection and ex- traction is challenging due to varying background~ low con- trast between text and non-text regions, and perspective distortion. In this paper, we propose a novel two phase approach to tackling this problem by discriminative fea- tures and edge density. The first phase firstly defines and extracts a novel feature called edge distribution entropy and then uses this feature to remove most non-text re- gions. The second phase employs a Support vector machine （SVM） to further distinguish real text regions from non- text ones. To generate inputs for SVM, additional three novel features are defined and extracted from each region： a foreground pixel distribution entropy, skeleton/size ra- tio, and edge density. After text regions have been de- tected, texts are extracted from such regions that are sur- rounded by sufficient edge pixels. A comparative study using two publicly accessible datasets shows that the pro- posed method significantly outperforms the selected four state of the art ones for accurate text detection and ex- traction.
ISSN:1022-4653