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Nepali Number Plate Recognition System Achieves 93% Accuracy

Researchers have developed a new Automatic Number Plate Recognition (ANPR) system specifically designed for Nepali license plates, which use the Devanagari script. The system employs a pipeline that first uses YOLO-based models for detecting license plates and characters, followed by a Convolutional Neural Network (CNN) classifier trained on 34 Devanagari characters. This approach achieved up to 93% recognition accuracy, demonstrating its effectiveness in real-world conditions and offering a scalable solution for traffic management in Nepal. AI

IMPACT This specialized ANPR system could improve traffic management and enforcement in regions using non-Latin scripts.

RANK_REASON The cluster contains an academic paper detailing a new computer vision model for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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Nepali Number Plate Recognition System Achieves 93% Accuracy

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The cluster contains an academic paper detailing a new computer vision model for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Satyasa Khadka, Sandhya Baral, Sudip Tiwari, Sharad Kumar Ghimire ·

    Character Recognition of Nepali Number Plate

    arXiv:2606.28946v1 Announce Type: new 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 p…