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2 September 1993 Artificial neural networks architecture for handwritten signature authentication
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It is frequently asked to individuals to prove their identity when writing official documents. This is done to avoid the use of someone else's signature and also to avoid that someone disowns a document that he has previously acknowledged. Texts are often typed, so it is not possible to authenticate these documents from handwriting. However, it is customary to append a mark authenticating the author of the document, thus showing that he agrees with the text of the document. Nowadays this mark is generally a handwritten signature, so it is interesting to devise an automatic and reliable system for the authentication of handwritten signatures appended on the numerous documents which are produced daily. The difficulty of the signature authentication problem is linked to the high number of writers, to the diversity of signatures to store, and also to the important variations between signatures from the same writer [Sabourin 90]. The authentication problem is different from the identification problem because the latter consists in determining the writer from his signature. In the authentication case, we know the writer who is supposed to have signed, as his name is written on the document, for example a check. So it is possible to access in a database to the signatures given by the writer to be used as reference signatures. Then, the authentication process consists in comparing the signature to the reference ones in order to judge if the supposed writer is really the author of the tested signature. The signature authentication can be used in several applications ; let us now focus on the verification of checks from the French Post Office. Our goal is to detect rough forgeries, which are signatures written by someone who is not imitating a genuine signature. Those rough forgeries are the most commonly found forgeries. Systems based on dynamic information (duration, speed of the signing, ...) are able to detect good imitations. In our application however, this dynamic information is lost because the image of the check contains only static information. Without major modifications, the authentication module of our system can be used by authentication systems based on other types of data such as digital fingerprints or dynamic information about the signatures.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hubert Cardot, Marinette Revenu, Bernard Victorri, and Marie-Josephe Revillet "Artificial neural networks architecture for handwritten signature authentication", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993);


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