YOLO and OCR-Based Automated Identity Document Verification

Abstract

Ensuring the authenticity of identity documents is crucial for secure digital transactions and regulatorycompliance. This paperpresentsa Comprehensive AutomatedDocumentVerificationSystemthatutilizesYOLO (YouOnlyLookOnce)for objectdetection andOCR(Optical Character Recognition) for dataextraction toverifyAadhaar cards,PANcards, and Voter IDcards. The systemautomates the verification process by detecting key document features, extractingrelevanttextualdata, andcross-verifyingitagainst predefined templates and databases. By integrating deep learning-based object detection with OCR, the proposed solution achieves high accuracy, efficiency, and scalability, reducing reliance on manual verification and minimizing fraud risks. Experimental results demonstrate the system’s robustness in detecting forged or tampered documents. This research contributes to improving digital security and streamlining identity verification in sectors such as banking, government services, and online KYC processes.

Introduction

Identity verification is essential in sectors like banking, e-governance, and finance. Traditional manual methods are slow, error-prone, and vulnerable to fraud. To address this, the study proposes an AI-based Comprehensive Automated Document Verification System (CADVS) that combines YOLO (You Only Look Once) for real-time object detection and OCR (Optical Character Recognition) for text extraction.

The system focuses on authenticating government-issued IDs (e.g., Aadhaar, PAN, Voter ID), aiming to minimize human intervention, improve accuracy, detect forged documents, and support large-scale deployment.

Key Features:

• Architecture: Modular design with separate input, processing, and output modules to ensure efficiency, scalability, and security.

• Technologies Used: Deep learning (YOLO), OCR, Python, OpenCV, TensorFlow/PyTorch, cloud services (AWS, GCP), and databases (MySQL, MongoDB).

• Security: Includes encryption, access controls, and authentication for data privacy and integrity.

• Deployment: Supports cloud, on-premise, and hybrid setups with Docker/Kubernetes for scalability and monitoring.

Related Works Review:

• Existing methods face challenges such as poor dataset diversity, text segmentation, noise, and identity fraud risks.

• Innovations like blockchain-steganography hybrids and deep learning-based text localization have been explored to boost ID security.

• Studies highlight the importance of high-quality datasets and combining traditional and AI-based techniques for robust verification.

Implementation:

• Steps: Data collection → preprocessing → model training → system integration → testing → deployment.

• YOLO & OCR Modules: YOLO for detecting ID document features; OCR for extracting and validating textual content.

• Performance: Processes documents in ~0.17 seconds with high accuracy.

Results & Analysis:

• Tested on 1,500 ID documents:

  • Aadhaar: 98.5% detection accuracy

  • PAN: 96.8%

  • Voter ID: 97.1%

  • OCR accuracy: 96.5%

  • Fraud detection: 95% success rate

• Compared to manual verification (12% error rate) and rule-based OCR (6%), CADVS achieves over 10% improvement in detection accuracy.

• Real-world deployment (e.g., banks) cut onboarding time by 40%.

Conclusion

This research presents a deep learning-based Automated Document Verification System that integrates YOLO for objectdetection andOCRfortextextraction.Theproposed system effectively automates document verification, reducinghuman intervention and mitigating fraudrisks.The experimentalresults confirm thatthe system achieves high accuracy in document detection, text extraction, and fraud detection, making it an efficient solution for large-scale identity verification applications. The significance of this study lies in its ability to enhance document authentication with minimal processing time, makingithighlyapplicablefor banking, e-governance,and secure identity management. The integration of deep learningmodelsensuresimproved accuracyandrobustness against document forgery, while the fraud detection mechanisms provide an additional layer of security. This research contributes to the growing field of automated identity verification, addressing the challenges of manual verificationandsecuritythreatsposedbydocumentforgery. Future enhancements to this system could include multilingualOCRsupporttoaccommodatevariousregional languages, thereby improving accessibility and usability. AI-driven anomaly detection techniques could be incorporated to enhance the fraud detection capabilities, makingthesystemmoreresilienttosophisticateddocument manipulations. Additionally, integrating blockchain technologyfor identitymanagement could further enhance the security and transparency of the verification process. Overall, the development of CADVS marks a significant advancement in automated document verification, paving thewayformoresecureandefficientidentityauthentication systems.Thefindingsofthisresearchdemonstratethatdeep learning-based verification systems are not only practical but also essential in combating identity fraud in today’s digital landscape. As technology continues to evolve, further refinements and optimizations will continue to enhance the system\'s efficiency, making it a reliable solution for various domains requiring secure and automated identity verification.

References

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Copyright

Copyright © 2025 Mohanapriya V, Mohanapriya N, Nandhini P, Dinesh V, Dr. N. Venkatesvara Rao. 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.