Leveraging Surrounding Context for Scene Text Detection

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Conference Proceeding
2013 20th IEEE International Conference on Image Processing, 2013, pp. 2264 - 2268
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Finding text in natural images has been a challenging task in vision. At the core of state-of-the-art scene text detection algorithms are a set of text-specific features within extracted regions. In this paper, we attempt to solve this problem from a different prospective. We show that characters and non-character interferences are separable by leveraging the surrounding context. Surrounding context, in our work, is composed of two components which are computed in an information-theoretic fashion. Minimization of an energy cost function yields a binary label for each region, which indicates the category it belongs to. The proposed algorithm is fast, discriminative and tolerant to character variations and involves minimal parameter tuning.
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