B Tech
Status of Project
A signature, being a unique and distinctive representation of an individual's identity, holds significant importance in various domains, such as finance, legal, and administrative sectors. Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature verification. Random distortions were generated in genuine images using an autoencoder to get forged signatures, which were passed to the classifier during training. The paper details all the pre-processing steps carried out on the image and shows various test results for changing the number of training sets of images. The average test accuracy for CEDAR dataset is 78% when the system was trained with 24 genuine images.
Students Involved
Kishan Kumar Mishra(1900520310028) Shailendra Singh (1900520310055) Shubham Singh (1900520320055) Sujeet Singh (1900520310062)
Final Submission Date