Abstract
Text information extracted from scene images is often the key clue for better performance of scene understanding and image retrieval. However, the clutter background and variations, which are intrinsic in scene images, make the natural scene character recognition task rather complicated. To overcome these disadvantages, we propose a novel approach for character recognition task in natural scene images. In the method, character classes are described by groups of local features using a probabilistic model. Structures of characters are represented by mutual positions of local features. For model learning, parameter estimating is done through expectation-maximization in a weak-supervised manner. Experiment results over datasets which includes both synthetic and authentic data demonstrate the validity of the approach.
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Zhang, B., Zhao, W., Liu, J., Wu, R., Tang, X. (2012). Character Recognition in Natural Scene Images Using Local Description. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_25
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DOI: https://doi.org/10.1007/978-3-642-31919-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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