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Deep Learning based Offline Signature Verification | IEEE Conference Publication | IEEE Xplore

Deep Learning based Offline Signature Verification


Abstract:

This paper presents the convolutional neural network for feature extraction and Support vector machine for the verification of offline signatures. The cropped signatures ...Show More

Abstract:

This paper presents the convolutional neural network for feature extraction and Support vector machine for the verification of offline signatures. The cropped signatures are used to train CNN for extracting features. The Extracted features are classified into two classes genuine or forgery using SVM. The the new signature is tested on GPDS signature data base using the trained SVM. The dabase contains signatures of 960 users and for each user there are 24 genuine signatures and 30 forgeries. The CNN network is trained with 300 users and signatures of 400 users are used for feature learning. These 400×20×25 signatures are used 90%to train and 10% to test SVM classifier.
Date of Conference: 08-10 November 2018
Date Added to IEEE Xplore: 15 August 2019
ISBN Information:
Conference Location: New York, NY, USA

References

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