With the improvement of computer computing power,the application of Artificial Intelligence technology in traditional fields promotes the development of intelligence.While traditional license plate recognition algorithms perform well in simple scenarios,their robustness significantly decreases in complex situations characterized by image distortion and blurriness.This research develops an efficient license plate recognition system suitable for complex scenarios by integrating the YOLOv5 Deep Learning model and the Tesseract-OCR library.The system is divided into two parts of license plate detection and character recognition,substantially improving recognition performance and system robustness under adverse conditions.Experimental results demonstrate that the system achieves average precision rates of 98.56%for license plate detection and 96.56%for character recognition across various complex scenarios,proving the effectiveness and superior performance of the approach.
Deep LearningYOLOv5Tesseract-OCRlicense plate recognitionrobustness
维普
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