Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies
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This study presents an automated license plate detection and recognition system, combining YOLOv10 for realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97% detection accuracy and real-time performance, demonstrating reliability in automated vehicle identification tasks such as traffic monitoring. This work underscores the synergy of YOLOv10’s detection efficiency and Tesseract’s OCR capabilities, offering a scalable solution for intelligent transportation systems.
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