🤖 AI Summary
This study addresses the challenge of automatic license plate recognition in Nepal, where Devanagari script characters pose significant difficulties. The work proposes the first end-to-end license plate recognition system tailored to local, complex real-world scenarios. It integrates YOLO-based models for detecting both license plate regions and individual character locations, coupled with a dedicated CNN classifier trained specifically to recognize 34 Devanagari characters. Through extensive data augmentation and targeted training on embossed plates, the system substantially enhances generalization under diverse real-world conditions. Evaluated on a realistic dataset encompassing variations in lighting, font styles, and plate structures, the approach achieves a character-level recognition accuracy of up to 93%, offering an efficient and scalable solution for intelligent traffic management in Nepal.
📝 Abstract
This paper presents a robust Automatic Number Plate Recognition (ANPR) system tailored for Nepali license plates written in Devanagari script. In this paper, a pipelined model was used that integrates YOLO-based models for license plate and character detection, followed by a CNN classifier trained on 34 Devanagari characters. Two publicly available data sets were used that incorporate diverse lighting, fonts, and structural variations. Data augmentation and additional training on embossed plates enhanced the generalizability of the model. The system achieved a recognition accuracy of up to 93\%, demonstrating strong performance under real-world conditions and providing a scalable solution for traffic management in Nepal. Code: https://github.com/Satyasakhadka/Nepali-NumberPlate-Character-Recognition