An Edge-based Text Region Extraction Algorithm for Indoor Mobile Robot Navigation
Using bottom-up image processing algorithms to predict human eye fixations and extract the relevant embedded information in images has been widely applied in the design of active machine vision systems. Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, and effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.
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on, 2003, pp. 611-616.
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and Y. K. Chung, "Scene text extraction in natural scene images using
hierarchical feature combining and verification," in Pattern Recognition,
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[1] K. Jung, K. I. Kim, and A. K. Jain, "Text information extraction in
images and video: a survey," Pattern Recognition, vol. 37, no. 5, pp.
977-997, 2004.
[2] K. Wang and J. A. Kangas, "Character location in scene images from
digital camera," Pattern Recognition, vol. 36, no. 10, pp. 2287-2299,
2003.
[3] R. Lienhart and A. Wernicke, "Localizing and segmenting text in images
and videos," IEEE Transaction on Circuits and Systems for Video
Technology, vol. 12, no. 4, pp. 256-268, 2002.
[4] Y. Zhong, K. Karu, and A. Jain, "Locating text in complex color
images," Pattern Recognition, vol. 28, no. 10, pp. 1523-1535, 1995.
[5] K. I. Kim, K. Jung, and J. H. Kim, "Texture-based approach for text
detection in images using support vector machines and continuously
adaptive mean shift algorithm," IEEE Trans. on Pattern Analysis and
Machine Intelligence (PAMI), vol. 25, no. 12, pp. 1631-1639, 2003.
[6] D. Chen, H. Bourlard, and J.-P. Thrian, "Text identification in complex
background using svm," in computer vision and pattern recognition
(CVPR-01), ser. Proceedings of the Int. Conf. on, vol. 2, December
2001, pp. 621-626.
[7] J. Gao and J. Yang, "an adaptive algorithm fot text detection from
natural scenes," in Computer Vision and Pattern Recognition, 2001.
CVPR 2001, ser. Proceedings of the 2001 IEEE Computer Society
Conference on, 2001, pp. II-84-II-89.
[8] A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliff,
NJ: Prentice Hall, 1989, ch. 9, pp. 356-357.
[9] N. Otsu, "A threshold selection method from gray-level histograms,"
IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp.
62-66, 1979.
[10] Intel r Open Source Computer Vision Library, Available:
http://www.sourceforge.net/projects/opencvlibrary (URL).
[11] J. Xi, X. S. Hua, X. R. Chen, L. Wenyin, and H. J. Zhang, "A video text
detection and recognition system," in Multimedia and Expo, 2001.
ICME 2001, ser. IEEE International Conference on, 2001, pp. 873-876.
[12] J. Gllavata, R. Ewerth, and B. Freisleben, "A robust algorithm for text
detection in images," in Image and Signal Processing and Analysis,
2003. ISPA 2003, ser. Proceedings of the 3rd International Symposium
on, 2003, pp. 611-616.
[13] K. C. Kim, H. R. Byun, Y. J. Song, Y. M. Choi, S. Y. Chi, K. K. Kim,
and Y. K. Chung, "Scene text extraction in natural scene images using
hierarchical feature combining and verification," in Pattern Recognition,
2004, ser. ICPR 2004. Proceedings of the 17th International Conference
on, vol. 2, Aug. 2004, pp. 679-682.
[14] L. Agnihotri and N. Dimitrova, "Text detection for video analysis," in
Content-Based Access of Image and Video Libraries, 1999. (CBAIVL
-99), ser. Proceedings. IEEE Workshop on, 1999, pp. 109-113.
[15] C. Wolf, J. M. Jolion, and F. Chassaing, "Text localization, enhancement
and binarization in multimedia documents," in Pattern Recognition,
2002, ser. Proceedings. 16th International Conference on, vol. 2, Aug.
2002, pp. 1037-1040.
[16] S. Messelodi and C. M. Modena, "Automatic identification and skew
estimation of text lines in real scene images," Pattern Recognition, vol.
32, no. 5, pp. 791-810, 1999
@article{"International Journal of Information, Control and Computer Sciences:55842", author = "Jagath Samarabandu and Xiaoqing Liu", title = "An Edge-based Text Region Extraction Algorithm for Indoor Mobile Robot Navigation", abstract = "Using bottom-up image processing algorithms to predict human eye fixations and extract the relevant embedded information in images has been widely applied in the design of active machine vision systems. Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, and effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.
", keywords = "Landmarks, mobile robot navigation, scene text, text localization and extraction.", volume = "1", number = "7", pages = "2045-8", }
