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Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm
December 2003 (vol. 25 no. 12)
pp. 1631-1639
Abstract— 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.
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Index Terms:
Text detection, image indexing, texture analysis, support vector machine, CAMSHIFT.
Citation:
Kwang In Kim, Keechul Jung, Jin Hyung Kim, "Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1631-1639, Dec. 2003, doi:10.1109/TPAMI.2003.1251157
