International Journal of Science, Engineering and TechnologyAuthors: P. Anusha, B. Niharika, A. Amitha, G. Anshuman
Abstract: This research focuses on developing a feasible and efficient solution for verifying handwritten signatures using advanced image processing techniques. The study is specifically limited to signatures that involve static inputs and outputs, meaning that it does not take into account dynamic elements such as writing speed or pressure. To identify the most suitable classifier for accurate signature verification, multiple machine learning models were explored, including the Multinomial Naïve Bayes Classifier (MNBC), Bernoulli Naïve Bayes Classifier (BNBC), Logistic Regression Classifier (LRC), Stochastic Gradient Descent Classifier (SGDC), and Random Forest Classifier (RFC). Each classifier was trained and evaluated using a publicly available signature dataset to ensure consistency and reliability in performance measurement. After rigorous testing, the Random Forest Classifier (RFC) demonstrated the highest accuracy, achieving a score of approximately 0.99. This suggests that RFC is the most effective model among those tested for distinguishing between genuine and forged signatures. On average, the system has proven to be highly successful in verifying signature images with a significant level of accuracy. The results of this study indicate that machine learning-based approaches, particularly RFC, can provide a reliable method for signature authentication, which could be beneficial in various real-time applications such as banking, legal document verification, and identity authentication.
DOI: https://doi.org/10.5281/zenodo.20423387
