Abstract
In this paper, we present a scene text extraction approach which can realize text localization and segmentation simultaneously. Two popular paradigms (machine learning method and rule-based method) are combined to achieve competitive performance. For a given image, a sliding window is used to detect scene text. The texture feature Local Binary Pattern is extracted to represent the content of each window, and an unbalanced SVM classifier is designed to identify candidate text regions. Then, candidate text windows are further verified using color contrast and binarized by an adaptive local thresholding computation to get candidate text connected components. Further, non-text ones among them are removed utilizing some empirical rules. Finally, text connected components are linked into text lines according to their spatial relationships and appearance similarities. The evaluation results on two challenging and standard datasets ICDAR 2003 and ICDAR 2011 demonstrate that the proposed approach can effectively detect and segment scene text with different sizes, fonts, colors and arrangement directions.
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Park, S.H., Kim, K.I., Jung, K., Kim, H.J.: Locating car license plates using neural networks. Electronics Letters 35(17), 1475–1477 (1999)
Liu, C.-L., Koga, M., Fujisawa, H.: Lexicon-driven segmentation and recognition of handwritten character strings for japanese address reading. IEEE Transactions on Pattern Analisis and Machine Intelligence 24(11), 1425–1437 (2012)
Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: towards a system for visually impaired persons. In: Proc. of 17th International Conference on Pattern Recognition, Cambridge, England, UK, August 23-26 (2004)
Liu, Q., Jung, C., Kim, S., Moon, Y., Kim, J.: Stroke filter for text localization in video images. In: Proc. of IEEE International Conference on Image Processing, Atlanta, GA, USA, October 8-11, pp. 1473–1476 (2006)
Li, Y., Lu, H.: Scene text detection via stroke width. In: Proc. of 21st International Conference on Pattern Recognition, Tsukuba, Japan, November 11-15, pp. 681–684 (2012)
Chen, X., Yang, J., Zhang, J., Waibel, A.: Automatic detection and recognition of signs from natural scenes. IEEE Transactions on Image Processing 13(1), 87–99 (2004)
Chen, X., Yuille, A.L.: Detecting and reading text in natural scenes. In: Proc. of Computer Vision and Pattern Recognition, Washington, DC, USA, June 27-July 2, pp. 366–373 (2004)
Mancas-Thillou, C., Gosselin, B.: Spatial and color spaces combination for natural scene text extraction. In: Proc. of the 13th International Conference on Image Proceedings, Atlanta, GA, October 8-11, pp. 985–988 (2006)
Neumann, L., Matas, J.: A method for text localization and recognition in real-world images. In: Proc. of Asian Conference on Computer Vision, New Zealand, November 8-12, pp. 30–35 (2010)
Carpenter, B., Case, C., Satheesh, S., Suresh, B., Wang, T., Wu, D.J., Ng, A.Y.: Text detection and character recognition in scene images with unsupervised feature learning. In: Proc. of the 11th International Conference on Document Analysis and Recognition, Beijing, China, September 18-21, pp. 440–445 (2011)
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Proc. of the 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, June 13-18, pp. 2963–2970 (2010)
Neumann, L., Matas, J.: Text localization in real-world images using efficiently pruned exhaustive search. In: Proc. of the 11th International Conference on Document Analysis and Recognition, Beijing, China, September 18-21, pp. 687–691 (2011)
Lucas, S.M.: Icdar 2005 text locating competition results. In: Proc. of the 8th International Conference on Document Analysis and Recognition, Seoul, Korea, August 29-September 1, pp. 80–84 (2005)
Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: Proc. of the 7th International Conference on Document Analysis and Recognition, Edinburgh, Scotland, UK, August 3-6, 2003, pp. 682–687 (2003)
Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: Proc. of the 25th IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, June 16-21, pp. 3538–3545 (2012)
Shahab, A., Shafait, F., Dengel, A.: ICDAR 2011 robust reading competition challenge 2: Reading text in scene images. In: Proc. of the 11th International Conference on Document Analysis and Recognition, pp. 1491–1496 (2011)
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Liu, X., Wang, W. (2013). Localize and Segment Scene Text. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_6
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DOI: https://doi.org/10.1007/978-3-319-03731-8_6
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