Abstract:
Personal identification and legal transaction verification employ each person’s unique signature. Most commonly used to authenticate cheques, drafts, certificates, approvals, letters, and legal documents. Because a signature is utilized in crucial tasks, its validity must be verified.
Preventing document forgery and fabrication in financial, legal, and commercial environments requires this verification. Traditionally, signatures were manually validated against copies of real signatures. As technology progresses, signature fraud and falsification may outgrow this simple procedure.
To solve this challenge, a new, effective tool is needed. Our Signature Verification System reduces human error in handwritten signature authentication.
The user must log in with basic information. To authenticate a signature, the user must enter two images: the original and the comparison. After entering the photographs, the user will see the results on the results page.
We used machine learning to classify the signature classification model. We’ll use CNN. We will compare a sample signature (approximately 100 sample photographs of the same signature) with other random signatures to test and see the outcomes.This project uses Django. The Front End uses HTML, CSS, and JavaScript. The back end uses MySQL. Python is the backend.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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