Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition


  • Suvodip Som Department of Electrical Engineering, Kalyani Government Engineering College, Nadia, West Bengal, India
  • Pritam Kumar Gayen Department of Electrical Engineering, Kalyani Government Engineering College, Nadia, West Bengal, India
  • Sudip Das Department of Electrical Engineering, JIS College of Engineering, Nadia, West Bengal, India



hybrid preprocess, weather-based preprocess, non-local means denoising, dark channel prior, adaptive histogram equalization


Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems.


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How to Cite

Suvodip Som, Pritam Kumar Gayen, and Sudip Das, “Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition”, Proc. eng. technol. innov., vol. 24, pp. 29–40, Apr. 2023.