NetraAadhaar: A Deep Learning-Driven Aadhaar Verification Platform for the Aid of Visually Impaired
Year: 2025
Type: article
Abstract: In today’s ever-changing world, visually impaired individuals face significant challenges in verifying personal documents, relying on others for assistance. The Aadhaar cards lack braille lettering, making independent verification difficult. Despite the widespread adoption of Aadhaar cards in India, research on assistive technologies for visually impaired users remains limited, making our work one of its kind. To address this, we introduce NetraAadhaar, a deep learning-based mobile application designed to assist visually impaired individuals in Aadhaar card verification. The framework consists of: (i) text region extraction from Aadhaar cards using YOLOv8, (ii) recognition of extracted text via Tesseract OCR engine, (iii) text-to-speech conversion for auditory verification, and (iv) the developed framework is fine-tuned and seamlessly integrated into an end-to-end mobile application for real-time use. NetraAadhaar achieves an mAP-50 score of 92.5% for text detection and an overall text recognition accuracy of 87. 79%, with a precision greater than 90% in key classes, demonstrating its robustness. Compared to traditional OCR-based assistive tools, NetraAadhaar offers higher accuracy, real-time performance, and an end-to-end automated pipeline making Aadhaar verification significantly more accessible and reliable for visually impaired users. NetraAadhaar empowers visually impaired users to verify their Aadhaar cards independently, reducing reliance on others and mitigating risks of identity fraud. The source code, dataset and developed application are publicly available for academic and research purposes at https://github.com/Adinp1213/NetraAadhar.
Source: IEEE Access
Authors: Aditya Patil, Tauseef Khan, Ayatullah Faruk Mollah
Institutions: VIT-AP University, Aliah University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4409762010