文章基本信息

标题：Review on: Enhanced Offline Signature Recognition Using Neural Network and SVM
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作者：Rapanjot Kaur ; Gagangeet Singh Aujla
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：3
页码：3648-3652
出版社：TechScience Publications
摘要：Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter. Signature verification systems can be categorized as offline (static) and online (dynamic). This thesis presents neural network and SVM with surf feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures can be handled as an image and recognized using computer vision and neural network and SVM with surf feature techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this thesis, off-line signature recognition & verification using neural network and SVM and surf feature is proposed, where the signature is captured and presented to the user in an image format [4, 5] . Signatures are verified based on parameters extracted from the signature using various image processing techniques. The Off-line Signature Recognition and Verification is implemented using Matlab. This work has been tested and found suitable for its purpose. For the implementation of this proposed work Matlab software is used
关键词：Signature verification; Indian script; Hindi;signatures; Document security; Neural Network and SVM