Abstract
This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.
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References
Zhong, Y., Karu, K., Jain, A.K.: Locating text in complex color images. Patt. Recog. 28(10), 1523–1535 (1995)
Li, H., Doermann, D., Kia, O.: Automatic Text Detection and Tracking in Digital Video. IEEE Trans. Image Proc. 9(1), 147–156 (2000)
Raju, S.S., Pati, P.B., Ramakrishnan, A.G.: Gabor Filter Based Block Energy Analysis for Text Extraction from Digital Document Images. In: Proc. Intl. Workshop DIAL, pp. 233–243 (2004)
Wu, V., Manmatha, R., Riseman, E.M.: TextFinder: an automatic system to detect and recognize text in images. IEEE Trans. PAMI 21(11), 1124–1129 (1999)
Clark, P., Mirmehdi, M.: Finding text using localised measures. In: Proc. British Machine Vision Conf., pp. 675–684 (2000)
Chen, X., Yuille, A.L.: Detecting and Reading Text in Natural Scenes. In: Proc. IEEE Intl. Conf. CVPR, vol. 2, pp. 366–373 (2004)
Shivakumara, P., Dutta, A., Tan, C.L., Pal, U.: A New Wavelet-Median-Moment based Method for Multi-Oriented Video Text Detection. In: Proc. Intl. Workshop on Document Analysis and Systems, pp. 279–286 (2010)
Gatos, B., Pratikakis, I., Kepene, K., Perantonis, S.J.: Text detection in indoor/outdoor scene images. In: Proc. Intl. Workshop CBDAR, pp. 127–132 (2005)
Zhu, K., Qi, F., Jiang, R., Xu, L., Kimachi, M., Wu, Y., Aizawa, T.: Using Adaboost to Detect and Segment Characters from Natural Scenes. In: Proc. Intl. Workshop CBDAR, pp. 52–59 (2005)
Pan, W., Brui, T.D., Suen, C.Y.: Text Detection from Scene Images Using Sparse Representation. In: Proc. ICPR, pp. 1–5 (2008)
Kasar, T., Ramakrishnan, A.G.: COCOCLUST: Contour-based Color Clustering for Robust Binarization of Colored Text. In: Proc. Intl. Workshop CBDAR, pp. 11–17 (2009)
Antonacopoulos, A., Karatzas, D.: Fuzzy Segmentation of Characters in Web Images Based on Human Colour Perception. In: Lopresti, D.P., Hu, J., Kashi, R.S. (eds.) DAS 2002. LNCS, vol. 2423, pp. 295–306. Springer, Heidelberg (2002)
ICDAR Robust reading competition data set (2003), http://algoval.essex.ac.uk/icdar/Competitions.html
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm
Kasar, T., Kumar, D., Prasad, A.N., Girish, D., Ramakrishnan, A.G.: MAST: Multi-scipt Annotation Toolkit for Scenic Text. In: Joint Workshop on MOCR and AND, pp. 113–120 (2011), software http://mile.ee.iisc.ernet.in/mast
Lucas, S.M.: ICDAR 2005 Text Locating Competition Results. In: Proc. ICDAR, pp. 80–84 (2005)
Neumann, L., Matas, J.: A Method for Text Localization and Recognition in Real-World Images. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 770–783. Springer, Heidelberg (2011)
Minetto, R., Thome, N., Cord, M., Fabrizio, J., Marcotegui, B.: SNOOPERTEXT: A multiresolution system for text detection in complex visual scenes. In: Proc. IEEE ICIP, pp. 3861–3864 (2010)
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Proc. IEEE Conf. CVPR, pp. 2963–2970 (2010)
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Kasar, T., Ramakrishnan, A.G. (2012). Multi-script and Multi-oriented Text Localization from Scene Images. In: Iwamura, M., Shafait, F. (eds) Camera-Based Document Analysis and Recognition. CBDAR 2011. Lecture Notes in Computer Science, vol 7139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29364-1_1
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DOI: https://doi.org/10.1007/978-3-642-29364-1_1
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