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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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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