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

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Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. / Kim, Kwang In; Jung, Keechul; Kim, Jin H.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, 2003, p. 1631-1639.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Kim, KI, Jung, K & Kim, JH 2003, '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. https://doi.org/10.1109/TPAMI.2003.1251157

APA

Kim, K. I., Jung, K., & Kim, J. H. (2003). 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, 25(12), 1631-1639. https://doi.org/10.1109/TPAMI.2003.1251157

Vancouver

Kim KI, Jung K, Kim JH. 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. 2003;25(12):1631-1639. doi: 10.1109/TPAMI.2003.1251157

Author

Kim, Kwang In ; Jung, Keechul ; Kim, Jin H. / Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003 ; Vol. 25, No. 12. pp. 1631-1639.

Bibtex

@article{37a7cbc9beba43df8721644da40ddc1a,
title = "Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm",
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.",
author = "Kim, {Kwang In} and Keechul Jung and Kim, {Jin H.}",
year = "2003",
doi = "10.1109/TPAMI.2003.1251157",
language = "English",
volume = "25",
pages = "1631--1639",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "IEEE Computer Society",
number = "12",

}

RIS

TY - JOUR

T1 - Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm

AU - Kim, Kwang In

AU - Jung, Keechul

AU - Kim, Jin H.

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

U2 - 10.1109/TPAMI.2003.1251157

DO - 10.1109/TPAMI.2003.1251157

M3 - Journal article

VL - 25

SP - 1631

EP - 1639

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 12

ER -