Efficient License Plate Detection and Recognition with YOLOv7 and OCR
Keywords:
YOLOv7, License Plate Recognition, Character Recognition, Performance Metrics, Optical Character RecognitionAbstract
This journal paper conducts a thorough investigation of YOLOv7's role in License Plate Recognition (LPR) for vehicles, with a specific focus on precision, recall, F1 score, mean Average Precision (mAP), and character recognition. Yet, the adaptability of Easy-OCR to various camera angles and its resource-efficient performance on compact devices present it as a valuable alternative. The future of character recognition in LPR systems should prioritize overcoming environmental challenges, license plate obstructions, and regional design variations. Solutions involve advanced pre-processing techniques and specialized algorithms tailored to address these issues. Simultaneously, refining the integration of Raspberry Pi, YOLOv7, and Easy OCR can lead to a scalable and efficient smart gate parking system. YOLOv7 excels in license plate detection, consistently achieving high precision (0.769), recall (0.8571), and F1 scores surpassing 0.66. Its steadily rising mAP (0.653) further underscores its ability to accurately localize license plates. In character detection, YOLOv7 outperforms Easy-OCR, boasting an average similarity rate of 94% compared to Easy-OCR's 80%. This innovative solution holds immense promise for enhancing parking efficiency, access control, and generating valuable urban data insights, ultimately addressing global parking challenges and improving the quality of urban living.
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