QR-Based Single Scan Resume Verification

Abstract

In today’s competitive job market, resume fraud and document manipulation have become serious concerns for employers and recruiters. To address this issue, this project proposes a QR Code-Based Resume Authentication System that ensures the authenticity and integrity of resumes submitted by candidates. The system generates a unique, tamper-proof QR code for each verified resume. When the QR code is scanned, it redirects the user to a secure web portal that validates the candidate’s credentials stored in a trusted database. This system provides a fast, reliable, and secure way for recruiters and institutions to verify resume authenticity with a single scan. It significantly reduces the risk of fake credentials, enhances transparency in the recruitment process, and builds trust between candidates and employers. The proposed solution can be extended to verify other official documents, making it a scalable and efficient approach to digital identity verification. The project is developed using HTML, CSS, JavaScript, and Bootstrap for the frontend, and Python Flask (or Node.js) for the backend. The SQLITE3 database is used to manage and store verified resume data. The QR code is generated using Python libraries such as qr code and scanned using web-based scanning APIs. Authentication is handled through encrypted tokens to maintain data confidentiality and prevent unauthorized access.

Introduction

The text presents a QR-Based Single Scan Resume Verification System designed to improve the efficiency, security, and reliability of credential verification in recruitment. Traditional resume verification methods are slow, manual, and vulnerable to fraud, while the rise of digital documentation has increased the risk of fake certificates and misrepresented qualifications. To address these issues, the proposed system uses QR code technology combined with advanced certificate verification techniques to provide instant and secure access to candidate credentials.

The system generates three different QR codes for each resume: one for direct resume viewing, one for uploading certificates, and one for accessing verified certificates only. These QR codes enable employers to instantly retrieve candidate information through a single scan, streamlining recruitment workflows. The system is built using the Django framework and follows a modular MVC architecture with separate components for user management, resume generation, certificate verification, and recruiter interaction.

A major contribution of the system is its multi-stage certificate verification pipeline. Verification begins with SHA-256 cryptographic hashing to detect duplicate certificates, followed by perceptual hashing (pHash) to identify visually altered or tampered documents. The system then preprocesses certificates using tools such as OpenCV, PyMuPDF, and Pillow, extracts text through Tesseract OCR, and validates important fields like candidate name, issuer, and date. Finally, Google Generative AI is used for multimodal analysis to classify certificates as REAL or FAKE based on confidence scores.

The methodology includes several modules: user management for registration and authentication, ATS-compatible resume generation using ReportLab, certificate verification, and a recruiter interface for searching and viewing candidate profiles. Recruiters can access resumes and verified certificates directly through QR scans. The system supports multiple file formats, including PDF, DOCX, JPG, JPEG, and PNG files.

Conclusion

This paper has presented a comprehensive QR-Based Single Scan Resume Verification System that addresses critical challenges in modern recruitment credential verification. The proposed system integrates multi-functional QR codes with sophisticated certificate verification capabilities to enable instant, accurate, and secure credential authentication. The key contributions of this research include: 1) Multi-Functional QR Architecture: The system generates three distinct QR codes from a single resume, each serving specific verification functions including resume access, certificate upload, and verified certificate viewing. This innovation reduces the verification complexity while maintaining comprehensive credential coverage. 2) Multi-Stage Verification Pipeline: The certificate verification module implements a six-stage validation pipeline that combines cryptographic hashing for duplicate detection with text extraction and field-level validation for authenticity assessment. The pipeline achieves 90% verification accuracy while maintaining high processing speed (500ms average). 3) Unified Verification Platform: The system provides an integrated platform that serves both candidates (resume management, certificate upload) and recruiters (search, verification, download) through a streamlined interface that reduces verification friction and improves user experience. 4) ATS Resume Generation: The system includes automated ATS-compatible resume generation capabilities that create standardized documents suitable for applicant tracking system integration, further streamlining the recruitment workflow. The proposed system demonstrates significant improvements over traditional manual verification approaches, reducing verification time from days to seconds while maintaining comparable accuracy levels. The architecture’s simplicity and interpretability ensure that verification outcomes can be explained and audited, addressing concerns around the black-box nature of machine learning approaches. In practical deployment, the system enables employers to verify candidate credentials during initial screening, reducing time-to-hire and improving candidate experience through immediate verification feedback. The QR-based access mechanism ensures that verification can be initiated from any location using standard smartphone cameras, eliminating the need for specialized verification hardware.

References

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Copyright

Copyright © 2026 P. Nava Bhanu, Kommineni Venkata Mohana Varshitha, Koppula Nanditha, Bikki Gnaneswari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.