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@article{198843,
        author = {B MOULESH and B YASHO VARDHAN and B JAGADEESH and V.ELAVENIL},
        title = {Signature Fraud Identification Using ML Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12406-12413},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198843},
        abstract = {Handwritten signatures have been considered one of the most accepted means of biometric authentication in the financial sector, legal documents, and other financial transactions. However, the vulnerability of signatures in forgery and identity theft necessitates the development of an automated mechanism of identifying fraud signatures. This research outlines a framework that can automatically differentiate genuine signatures from forged ones using the power of machine learning. The steps involved in the proposed system match those of a general verification approach. They include data acquisition, pre-processing, feature extraction, and classification. During pre-processing, the quality of the signature is improved through noise removal, grayscale conversion, binarization, normalization. Relevant discriminative features of the signature are identified using texture as well as structural descriptors. Finally, machine learning approaches such as the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models are trained on the identified features. The trained model classifies the input signature as genuine or fake.},
        keywords = {Signature verification; Signature fraud detection; Machine learning; Offline signature recognition; Image preprocessing; Feature extraction; Pattern recognition; Support vector machine; Convolutional neural network; Biometric authentication; Forgery detection; Similarity matching; FAR; FRR; Document Authentication},
        month = {April},
        }