Abstract
As the scene text detection and localization is one of the most important steps in text information extraction system, it had been widely utilized in many computer vision tasks. In this paper, we introduce a new method based on the maximally stable extremal regions (MSERs). First, a coarse-to-fine classier estimates the text probability of the ERs. Then, a pruning algorithm is introduced to filter non-text MSERs. Secondly, a hybrid method is performed to cluster connected components (CCs) as candidate text strings. Finally, a fine design classifier decides the text strings. The experimental results show our method gets a state-of-the-art performance on the ICDAR2005 dataset.
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Acknowledgements
The authors thank the anonymous reviewers and editor who provided many valuable comments and suggestions. This work is funded by Scientific Research Programs of the Higher Education Institution of XinJiang (grant nos. XJEDU2014S006 and XJEDU2014I004).
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Zhou, G., Liu, Y., Shi, F., Hu, Y. (2016). Scene Text Detection Based on Text Probability and Pruning Algorithm. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_67
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DOI: https://doi.org/10.1007/978-3-319-42297-8_67
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