A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints
In this paper we propose a robust text segmentation method in complex background. The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.
KeywordsChinese Character Complex Background Text Block Text Detection Image Layer
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- 2.Wu, V., Manmatha, R., Riseman, E.: Finding text in images. In: Proceedings of ACM International Conference on Digital Libraries, Philadelphia, pp. 1–10 (1997)Google Scholar
- 5.Ye, Q., Gao, W., Wang, W., Zeng, W.: A robust text detection algorithm in images and video frames. In: 4th IEEE Pacific-Rim Conference on Multimedia, Singapore (2003)Google Scholar
- 7.Lienhart, R., Wernicke, A.: Localizing and segmenting text in images and videos. IEEE Trans. on Circuits and Systems for Video Technology 12(4) (April 2002)Google Scholar
- 9.Trier, O.D., Jain, A.K.: Goal-directed evaluation of binarization methods. IEEE Trans. on Pattern Recognition and Machine Intelligence. 12 (December 1995)Google Scholar
- 10.Tsai, C.M., Lee, H.J.: Binarization of color document images via luminance and saturation color features. IEEE Transactions on Image Processing 11(4) (2002)Google Scholar
- 11.Lienhart, R.: Video OCR: a survey and practitioner’s guide. In: Video Mining, October, pp. 155–184. Kluwer Academic Publisher, Dordrecht (2003)Google Scholar
- 13.Gao, J., Yang, J.: An adaptive algorithm for text detection from natural scenes. In: Computer Vision and Pattern Recognition, December 2001, vol. 2 (2001)Google Scholar
- 14.Ye, Q., Gao, W., Huang, Q.: Automatic text segmentation from complex background. In: IEEE International Conference on Image Processing, Singapore (October 2004)Google Scholar
- 15.Sato, T., Kanade, T., Hughes, E., Smith, M.: Video OCR for digital news archives. In: IEEE Workshop on Content-based Access of Image and Video Databases, Bombay, India, January, pp. 52–60 (1998)Google Scholar
- 16.Tang, X., Gao, X., Liu, J., Zhang, H.: A spatial-temporal approach for video caption detection and recognition. IEEE Trans. on Neural Networks 13(4) (July 2002)Google Scholar