Abstract
The Handwritten Signature is a special sign used by humans and may contain letters, curves, or both. The main usage of handwritten signature is a proof of identification, especially when dealing with official documents and treatments. To recognize a signature means to identify the person who uses this sign. Signature recognition has many applications, such as: transactions and checks in banking systems, forensic caseworks, personal authentication and verification. This work proposes a new method to recognize handwritten signature in an offline manner. The centroid of two local binary vectors, the horizontal vector and the vertical vector are calculated. Three different tests are accomplished for this method. Soft evaluation test, Hard evaluation test, and a Combined test. The gained results from the three of these tests are encouraging. It reached for what is so called the Combined test to 94.8275% of success rate for 928 digital images of handwritten signatures with processing time for single sample reaches to 0.146 in milliseconds.
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Kamal, N.N., George, L.E. (2018). Offline Signature Recognition Using Centroids of Local Binary Vectors. In: Al-mamory, S., Alwan, J., Hussein, A. (eds) New Trends in Information and Communications Technology Applications. NTICT 2018. Communications in Computer and Information Science, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-01653-1_16
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