基于YOLO与CNN的尼泊尔车牌字符识别系统，准确率达93%。
Character Recognition of Nepali Number Plate
- 采用YOLO检测车牌与字符，CNN分类34个天城文字符。
- 在光照、字体多变条件下实现93%识别准确率。
- 适合用于尼泊尔交通管理系统的实际部署。
本文提出一种针对尼泊尔车牌（使用天城文书写）的鲁棒自动车牌识别（ANPR）系统。采用流水线模型，先用基于YOLO的模型检测车牌及字符，再通过训练于34个天城文字符的CNN分类器进行识别。实验使用两个公开数据集，涵盖多样光照、字体和结构变化；通过数据增强及压印车牌额外训练，提升模型泛化能力。系统在真实场景下达到最高93%的识别准确率，展现出良好性能，为尼泊尔交通管理提供可扩展解决方案。
原文摘要 · Abstract (English)
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
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