New Approach Based on Texture and Geometric Features for Text Detection
Abstract
Due to the huge amount of data carried by images, it is very important to detect and identify the text region as accurately as possible before performing any character recognition. In this paper we describe a text detection algorithm in complex background. It is based on texture and connected components analysis. First we abstract texture regions which usually contain text. Second, we segment the texture regions into suitable objects; the image is segmented into three classes. Finally, we analyze all connected components present in each binary image according to the three classes with the aim to remove non-text regions. Experiments on a benchmark database show the advantages of the new proposed method compared to another one. Especially, our method is insensitive to complex background, font size and color; and offers high precision (83%) and recall(73%) as well.
Keywords
Text detection text localization feature extraction texture analysis geometric analysisReferences
- 1.Liu, Y., Goto, S., Ikenaga, T.: A Contour-Based Robust Algorithm for Text Detection in Color Images. IEICE Transactions 89-D(3), 1221–1230 (2006)CrossRefGoogle Scholar
- 2.
- 3.Wu, V., Manmatha, R., Riseman, E.M.: Finding text in images. In: DL 1997: Proceedings of the second ACM international conference on Digital libraries, New York, NY, USA, pp. 3–12. ACM, New York (1997)CrossRefGoogle Scholar
- 4.Chen, D.T., Bourlard, H., Thiran, J.-P.: Text identification in complex background using SVM. In: International Conference on Computer Vision and Pattern Recognition 2001, pp. 621–626 (2001)Google Scholar
- 5.Ye, Q., Gao, W., Wang, W., Zeng, W.: A robust text detection algorithm in images and video frames. In: Joint Conference of Fourth International Conference on Information Communications and Signal Processing and Pacific-Rim Conference on Multimedia, Singapore (2003)Google Scholar
- 6.Smith, M.A., Kanade, T.: Video skimming for quick browsing based on audio and image characterization, Carnegie Mellon University, Pittsburgh, PA, Technical Report CMU-CS-95-186 (July 1995)Google Scholar
- 7.Zhong, Y., Karu, K., Jain, A.K.: Locating text in complex color images. In: ICDAR ’95: Proceedings of the Third International Conference on Document Analysis and Recognition, Washington, DC, USA, vol. 1, p. 146. IEEE Computer Society, Los Alamitos (1995)CrossRefGoogle Scholar
- 8.Jain, A.K., Yu, B.: Automatic text location in images and video frames. Pattern Recognition 31, 2055–2076 (1998)CrossRefGoogle Scholar
- 9.David, H.L., Doermann, D., Kia, O.: Automatic text detection and tracking in digital video. IEEE Transactions on Image Processing 9(1) (2000)Google Scholar
- 10.Zhong, Y., Zhang, H., Jain, A.K.: Automatic caption localization in compressed video. IEEE Trans. Pattern Anal. Mach. Intell. 22(4), 385–392 (2000)CrossRefGoogle Scholar
- 11.Kim, K.I., Jung, K., Kim, J.H.: Texture-based approach for text detection in image using support vector machine and continuously adaptive mean shift algorithm. IEEE Transaction on PAMI 25, 1631–1639 (2003)Google Scholar
- 12.Li, X., Wang, W., Jiang, S., Huang, Q., Gao, W.: Fast and effective text detection. In: ICIP, pp. 969–972. IEEE, Los Alamitos (2008)Google Scholar
- 13.Ye, Q., Jiao, J., Huang, J., Yu, H.: Text detection and restoration in natural scene images. J. Vis. Comun. Image Represent. 18(6), 504–513 (2007)CrossRefGoogle Scholar
- 14.Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: Towards a system for visually impaired persons. In: ICPR (2), pp. 683–686 (2004)Google Scholar
- 15.Hanif, S., Prevost, L.: Text detection and localization in complex scene images using constrained adaboost algorithm, pp. 1–5 (2009)Google Scholar