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<p>Rapid improvements in the industry have created a demand for intelligent and energy-efficient automation solutions, notably in industrial access control systems. This paper proposes a unique framework for Non-Latin Number Plate Recognition that is designed specifically for industrial vehicles and addresses the issues given by different alphanumeric patterns across countries. The suggested system uses a modified You Only Look Once (YOLO)-based object identification algorithm for exact number plate location, together with Google Cloud Vision for robust text extraction. The proposed&#xa0;framework achieves 97% accuracy and 99% recall while preserving low computational overhead and energy economy. Its lightweight architecture allows for simple scalability along with integration into existing industrial ecosystems, making it a low-cost and dependable option for Automatic License Plate Recognition (ALPR) in industrial settings. This study adds to the advancement of next-generation access control systems by balancing high detection accuracy with optimal processing efficiency.</p> Graphical Abstract <p></p>

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An Intelligent Energy-Efficient Framework for Real Time Non-Latin License Plate Recognition in Industrial Vehicle Access Control

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Eshan Sengupta,
  • Munish Saini,
  • Tonmoy Rudra

摘要

Rapid improvements in the industry have created a demand for intelligent and energy-efficient automation solutions, notably in industrial access control systems. This paper proposes a unique framework for Non-Latin Number Plate Recognition that is designed specifically for industrial vehicles and addresses the issues given by different alphanumeric patterns across countries. The suggested system uses a modified You Only Look Once (YOLO)-based object identification algorithm for exact number plate location, together with Google Cloud Vision for robust text extraction. The proposed framework achieves 97% accuracy and 99% recall while preserving low computational overhead and energy economy. Its lightweight architecture allows for simple scalability along with integration into existing industrial ecosystems, making it a low-cost and dependable option for Automatic License Plate Recognition (ALPR) in industrial settings. This study adds to the advancement of next-generation access control systems by balancing high detection accuracy with optimal processing efficiency.

Graphical Abstract