A novel image text extraction method based on k-means clustering

被引：20

作者：

Song, Yan ^{[1
]}

Liu, Anan ^{[1
]}

Pang, Lin ^{[1
]}

Lin, Shouxun ^{[1
]}

Zhang, Yongdong ^{[1
]}

Tang, Sheng ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China

来源：

7TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE/ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICIS.2008.31

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Texts in web pages, images and videos contain important clues for information indexing and retrieval. Most existing text extraction methods depend on the language type and text appearance. In this paper, a novel and universal method of image text extraction is proposed A coarse-to-fine text location method is implemented Firstly, a multi-scale approach is adopted to locate texts with different font sizes. Secondly, projection profiles are used in location refinement step. Color-based k-means clustering is adopted in text segmentation. Compared to grayscale image which is used in most existing methods, color image is more suitable for segmentation based on clustering. It treats corner-points, edge-points and other points equally so that it solves the problem of handling multilingual text. It is demonstrated in experimental results that best performance is obtained when k is 3. Comparative experimental results on a large number of images show that our method is accurate and robust in various conditions.

引用

页码：185 / 190

页数：6

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