Automatic Number Plate Recognition in Hazy Conditions

This study explores automatic number plate recognition (ANPR) in hazy conditions, emphasizing the critical role of dehazing methods in enhancing model accuracy and resilience, especially in challenging weather scenarios like winter. Through a systematic evaluation of ANPR techniques, the study identifies effective dehazing strategies tailored to address specific environmental obstacles, underscoring the significance of customized approaches for optimal performance. By integrating the dark channel prior technique, the model's robustness in adverse weather conditions is significantly improved, laying the foundation for future advancements in ANPR technology through targeted research on hardware optimization and enhanced dehazing methodologies.