Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm

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The current paper presents a novel texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used; rather, the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, text regions are identified by applying a continuously adaptive mean shift algorithm (CAMSHIFT) to the results of the texture analysis. The combination of CAMSHIFT and SVMs produces both robust and efficient text detection, as time-consuming texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be texture-analyzed.
Publisher
IEEE COMPUTER SOC
Issue Date
2003-12
Language
English
Article Type
Article
Keywords

FACE DETECTION; VIDEO

Citation

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.25, no.12, pp.1631 - 1639

ISSN
0162-8828
URI
http://hdl.handle.net/10203/10240
Appears in Collection
CS-Journal Papers(저널논문)
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