Authors : S. M. Jawake; Gauri Kulkarni; Rujuta Deshmukh; Renuka Raut

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/272p3u8a

Scribd : https://tinyurl.com/3ckn6n9v

DOI : https://doi.org/10.38124/ijisrt/26mar401

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The Centralized Admission Process (CAP) used in higher education institutions depends largely on verifying student documents, a task that is traditionally manual, time-consuming, and prone to human error. Although national digital document repositories ensure the authenticity of academic records, they lack automated mechanisms for validating critical contextual parameters such as document expiry dates, name consistency across certificates, and issuing authority legitimacy. To address this limitation, this paper proposes VeriScan, an intelligent and automated document verification framework. The proposed system securely integrates with a digital document repository to retrieve authentic records and applies an intelligent verification layer using optical character recognition and a rules-based validation engine. This layer automatically extracts and verifies key metadata, detecting discrepancies without human intervention. Upon successful verification, a unique and secure digital credential in the form of a QR code is generated for each applicant, enabling administrators to instantly confirm verification status. The proposed approach significantly reduces processing time, minimizes manual errors, and enhances the transparency and integrity of the admission process. Furthermore, the framework demonstrates potential for future extension into government services, financial verification, and corporate onboarding systems.

Keywords : Document Verification, DigiLocker, Optical Character Recognition, Automated Admissions, QR Code Authentication, Artificial Intelligence.

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