Abstract
In this paper, we propose a method leveraging context information for text detection in natural scene images. Most of the existing methods just utilize the hand-engineered features to describe the text area, but we focus on building a confidence map model by integrating the candidate appearance and the relationships with its adjacent candidates. Three layers of filtering strategy is designed to judge the category of the text candidates, which can remove abundant non-text regions. In order to retrieve the missing text regions, a context fusion step is performed. Finally, the remaining connected components (CCs) are grouped into text lines and are further verified, and then the text lines are broken into separate words. Experimental results on two benchmark datasets, i.e., ICDAR 2005, ICDAR 2013, demonstrate that the proposed approach has achieved the competitive performances with the state-of-the-art algorithms.
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Wang, R., Sang, N., Gao, C., Kuang, X., Xiang, J. (2014). Text Detection in Natural Scene Images Leveraging Context Information. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_47
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DOI: https://doi.org/10.1007/978-3-662-45643-9_47
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