Research Article | Open Access
SIGNATURE VERIFICATION WITH IMAGE PROCESSING USING PYTHON
Mrs. A ANITHA REDDY, MAMIDI PRAFUL REDDY, PRERANA GANJI, RAMAGALA NAVEEN KUMAR, BHOOMI
Pages: 817-824
Abstract
This research was conducted to find a feasible solution to verify handwritten signatures. The scope has been narrowed down to signatures which contain static inputs and outputs. Several classification methods such as Multinomial Naive Bayes Classifier (MNBC), Bernoulli Naive Bayes Classifier (BNBC), Logistic Regression Classifier (LRC), Stochastic Gradient Descent Classifier (SGDC) and Random Forest Classifier (RFC) were implemented to identify the most suitable classifier to verify handwritten signatures. The classifiers were trained and tested using a signature database available for public use. The best performance was obtained from RFC with an accuracy score around 0.99. For an average, the system created has been successful in verifying signature images provided with a considerable accuracy level.
Keywords
This research was conducted to find a feasible solution to verify handwritten signatures.