Abstract
We propose an approach dedicated to recognize characters from binary images by an hybrid strategy. A statistical method is used to identify the global shape of each alphanumeric symbol. The recognition is managed by a Hierarchical Neural Network (HNN), that is able to deal with topological errors in the contour extraction. This strategy is extremely efficient for the majority of the classes: the recognition rate reaches about 99.5%. However, the performances sensitively decrease for ’similar characters’, i.e. ’8’/’B’. In that case, we adopt a strategy that revolves around decomposing the characters into structural elements. The Reeb graph generated from the binary images and a simple polygonal approximation permit to capture both topological and geometrical relevant features. The classification stage is carried out by a boosting algorithm.
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Thome, N., Vacavant, A. (2007). A Combined Statistical-Structural Strategy for Alphanumeric Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_52
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DOI: https://doi.org/10.1007/978-3-540-76856-2_52
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