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