Bangladeshi License Plate Detection and Recognition Using YOLO Variants and Enhanced OCR with Model Interpretability

Abstract

This study presents a sophisticated framework for automatic license plate recognition (ALPR) designed for Bangladeshi vehicle registration plates, tackling the intricacies of Bangla script and diverse environmental conditions. We employ three YOLO variants YOLOv5, YOLOv8, and YOLOv11for accurate license plate detection, yielding mean Average Precision (mAP50) scores of 0.955, 0.961, and 0.950, respectively, on a primary dataset of Bangladeshi images. Detected plates undergo meticulous preprocessing with OpenCV, encompassing grayscale conversion, adaptive thresholding, contour detection, and Gaussian blur to mitigate noise and enhance text clarity. These steps are critical to address challenges such as variable lighting, shadows, and plate degradation. A tailored Optical Character Recognition (OCR) pipeline, specifically adapted for Bangla script, achieves a character-level accuracy of 89%. The OCR modifications include enhanced character segmentation and a Bangla-specific language model to overcome the complexities of Bangla’s nonlinear script, which poses significant challenges for standard OCR systems due to its conjunct characters and intricate glyphs. The framework exhibits robustness against occlusions, non-standard plate formats, and urban environmental variability, offering a viable solution for intelligent transportation systems in Bangladesh. Comparative evaluation of YOLO variants highlights YOLOv8’s superior mAP50 and YOLOv11’s high precision, informing their suitability for real-time applications. This work establishes a foundation for scalable ALPR, with potential to enhance traffic management and law enforcement in Bangladesh.