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

Kwang In Kim, Keechul Jung, Jin H. Kim

Research output: Contribution to journalArticle

346 Citations (Scopus)

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, but 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.
Original languageEnglish
Pages (from-to)1631-1639
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume25
Issue number12
DOIs
Publication statusPublished - Dec 2003

Fingerprint Dive into the research topics of 'Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm'. Together they form a unique fingerprint.

Cite this