Comparative Analysis of EasyOCR and TesseractOCR for Automatic License Plate Recognition using Deep Learning Algorithm

License plate recognition are used in toll plaza, surveillance cameras, intelligent car parking, etc,. This paper proposes three modules for number plate recognition: Image acquisition, License plate detection and Character recognition. Firstly, a pytorch library OpenCV is used for retrieving the data. YOLOv5, a family of You Only Look Once (YOLO) model is used for detecting the number plate. Finally, OCR methods i.e., Tesseract OCR and EasyOCR are used for recognizing and extracting the characters from the number plates. A dataset from github is used for training and testing the proposed model. The result shows that EasyOCR has resulted in more than 95% accuracy for predicting the number plate when compared to Tesseract OCR which has only resulted in 90% accuracy. Hence, EasyOCR outperforms Tesseract OCR as it uses deep learning approach for object recognition and it is efficient in real time prediction.