Text Particles Multi-band Fusion for Robust Text Detection

  • Authors:

  • Pengfei Xu;Rongrong Ji;Hongxun Yao;Xiaoshuai Sun;Tianqiang Liu;Xianming Liu

  • Affiliations:

  • School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China 150001

  • Venue:
  • ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
  • Year:
  • 2008

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Abstract

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.