YOLOv8-Based License Plate Recognition for Bangladeshi Vehicles

2025 · doi:10.22541/au.173748276.69471512/v1
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This preprint investigates automatic license plate recognition in Bangladesh, targeting difficulties created by Bangla script complexity and low-resolution CCTV imagery. The authors develop a YOLOv8-based model that performs license plate localization, character segmentation, and Bangla OCR recognition using Easy-OCR, trained on 2,600 images collected and augmented via Roboflow across conditions such as low resolution, harsh weather, and partial obstruction. They report 94.8% detection and recognition accuracy, with the model distinguishing license plates from other rectangular objects on vehicles. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Automatic License Plate Recognition (ALPR) in Bangladesh faces challenges due to the complexity of Bangla script and low-resolution CCTV footage. This research introduces a YOLOv8-based deep learning model tailored for Bangladeshi license plates, enhancing plate localization, character segmentation, and Bangla script recognition using Easy-OCR. The model leverages Roboflow for data collection, annotation, and augmentation, training on a dataset of 2600 images captured under diverse conditions, including low resolution, harsh weather, and partial obstructions. The model distinguishes license plates from other rectangular objects on vehicles, achieving 94.8% detection and recognition accuracy. These results demonstrate the system's robustness in real-world scenarios, contributing to improved road safety, traffic management, and law enforcement in Bangladesh, marking a significant advancement in ALPR technology for the region. This research marks a significant advancement in ALPR technology for Bangladesh, contributing to improved road safety, efficient traffic management, and enhanced law enforcement capabilities.
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Abstract

Automatic License Plate Recognition (ALPR) in Bangladesh faces challenges due to the complexity of Bangla script and low-resolution CCTV footage. This research introduces a YOLOv8-based deep learning model tailored for Bangladeshi license plates, enhancing plate localization, character segmentation, and Bangla script recognition using Easy-OCR. The model leverages Roboflow for data collection, annotation, and augmentation, training on a dataset of 2600 images captured under diverse conditions, including low resolution, harsh weather, and partial obstructions. The model distinguishes license plates from other rectangular objects on vehicles, achieving 94.8% detection and recognition accuracy. These results demonstrate the system's robustness in real-world scenarios, contributing to improved road safety, traffic management, and law enforcement in Bangladesh, marking a significant advancement in ALPR technology for the region. This research marks a significant advancement in ALPR technology for Bangladesh, contributing to improved road safety, efficient traffic management, and enhanced law enforcement capabilities. Supplementary Material File (2025_ijcat-247414.pdf) - Download - 378.28 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

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Authors Metrics & Citations Metrics Article Usage 353views 133downloads Citations Download citation istiak ahamed, Gazi mohammad ismail. YOLOv8-Based License Plate Recognition for Bangladeshi Vehicles. Authorea. 21 January 2025. DOI: https://doi.org/10.22541/au.173748276.69471512/v1 DOI: https://doi.org/10.22541/au.173748276.69471512/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

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