Texts in images and videos usually carry important information for visual content understanding and retrieval. Two main restrictions exist in the state-of-the-art text detection algorithms: weak contrast and text-background variance. This paper presents a robust text detection method based on text particles (TP) multi-band fusion to solve there problems. Firstly, text particles are generated by their local binary pattern of pyramid Haar wavelet coefficients in YUV color space. It preserves and uniforms text-background contrasts while extracting multi-band information. Secondly, the candidate text regions are generated via density-based text particle multi-band fusion, and the LHBP histogram analysis is utilized to remove non-text regions. Our TP-based detection framework can robustly locate text regions regardless of diversity sizes, colors, rotations, illuminations and text-background contrasts. Experiment results on ICDAR 03 over the existing methods demonstrate the robustness and effectiveness of the proposed method. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Xu, P., Ji, R., Yao, H., Sun, X., Liu, T., & Liu, X. (2008). Text particles multi-band fusion for robust text detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5112 LNCS, pp. 587–596). https://doi.org/10.1007/978-3-540-69812-8_58
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