Abstract
Offline signature recognition is a very difficult task due to normal variability in signatures and the unavailability of dynamic information regarding the pen path. In this paper, a technique for signature recognition is proposed based on shape context that summarizes the global signature features in a rich local descriptor. The proposed system reaches 100 % accuracy but had some scalability problems as a result of the correspondence problem between the queried signature and all the data set signatures. To address the scalability problem of using shape context for signature matching, the proposed method speeds up the matching stage by representing the shape context features as a feature vector and then applies a clustering algorithm to assign signatures to their corresponding classes.
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Omar, A.M., Ghanem, N.M., Ismail, M.A., Ghanem, S.M. (2015). Arabic-Latin Offline Signature Recognition Based on Shape Context Descriptor. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_3
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DOI: https://doi.org/10.1007/978-3-319-20904-3_3
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