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A person's signature is a significant biometric trait that can be used to identify the person, often required in financial transactions and insurance-related activities. It is well-known that during such transactions, handwritten forgery signatures made on behalf of a genuine user results stealing the money or other means of wealth which was safely kept in the bank or other institutions. Therefore, it is necessary to have a vital safeguard like an automated signature verification system against malicious offenders who threaten society. This paper reports a writer independent offline signature verification system that makes use of genuine and forged signatures written in Manipuri script. Further, a combination of handcrafted geometric features and the features extracted using Convolutional Neural Network (CNN) is used. Then, the combined features' feature space is made optimal using the Genetic Algorithm (GA). This system has achieved a very high-level performance using an ensemble of four pattern classifiers, Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes learning, and Decision Tree Learning. Ensembling of the classifiers is done using logical OR rule and Majority Voting. Experiments are conducted on an original database consisting of Manipuri signatures of 81 individuals. Experimental results are compelling, while the proposed offline signature verification system is compared with the existing system.
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