A Digital Solution to Wrong E-Challan

Abstract: The rapid expansion of automated traffic enforcement infrastructure in Indian cities has significantly improved law compliance but has simultaneously exposed a critical systemic flaw: citizens receive e-challans with incorrect vehicle details, wrong violation types, or fraudulent claims with no easy mechanism for verification or dispute. When an innocent citizen receives a wrongly issued e-challan, they face significant legal, financial, and administrative hardship. This paper proposes 'A Digital Solution to Wrong E-Challan', a comprehensive verification and dispute resolution framework engineered to prevent false challan consequences at their root. The system integrates an AI-powered evidence capture module using EasyOCR for number plate recognition with multiple preprocessing strategies (CLAHE, bilateral filtering, Otsu thresholding), a vehicle attribute verification engine using YOLOv8 for vehicle type classification and HSV color space analysis with K-means clustering for cross-validation of vehicle color against a MySQL-backed vehicle registration database containing 30+ sample RC records. A public verification portal allows citizens to authenticate challans without login, and an automated SMS notification module using Fast2SMS API provides real-time citizen communication. A structured dispute resolution workflow enables citizens to report incorrect challans with categorized complaints (wrong vehicle, wrong location, wrong violation, duplicate, fake challan) with status tracking from PENDING through REVIEWED to RESOLVED/REJECTED. The Flask-based backend implements 8 route modules including detect(), generate_challan(), verify_challan(), and report_challan() with session-based authentication and UUID-based verification tokens. Experimental results demonstrate a challan verification accuracy of 91% on plate detection across multiple preprocessing strategies, 87% on HSV-based color matching, and 94% on YOLOv8-based vehicle type classification, with an end-to-end processing latency of under 3 seconds per verification. The proposed system is designed as a software-first, web-accessible solution compatible with existing traffic enforcement infrastructure, making it highly deployable across Indian cities without additional hardware investment.

Keywords: E-Challan, Automatic Number Plate Recognition, EasyOCR, YOLOv8, HSV Color Detection, Fast2SMS, Dispute Resolution, Public Verification, Flask, MySQL, Intelligent Transportation Systems.