@article{MAKHILLJEAS201914817663,
    title = {Offline Recognition of Handwritten Signatures Based on the SURF and
SVM Algorithms},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {8},
    pages = {2687-2694},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2687.2694},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2687.2694},
    author = {Hossam E.,Ali Khaleel and},
    keywords = {SURF,SVM,feature extraction and BOW,descriptors,dictionary,recognition},
    abstract = {Signature biometric becomes one of most relevant security factors in modern ubiquitous applications.
Signature recognition is the process of using this biometric to verifying and identifying people accurately.
There are several challenges associated with reliable recognition of these signatures. In this study, we have
proposed a new approach for offline signature recognition. The SURF algorithm is used in this approach to
specify invariant key points and descriptors while SVM algorithm is used for classification purposes. In
addition, BOW algorithm is used to build dictionary of the most discriminative features of the handwritten
signatures. Feature extraction and recognition are the key elements in the proposed approach for offline
signature recognition. Our approach outperforms compared state-of-art approaches by providing 98.75%
signature recognition accuracy.}
    }