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.
