Real-time image of vehicle number plate for seamless smart motor parking in challenging environments

¹ Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Taichung 41170, Taiwan, R.O.C
² Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 41170, Taiwan, R.O.C
³ Department of Refrigeration, Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung 41170, Taiwan R.O.C

^* Corresponding author: wjluo@ncut.edu.tw

Abstract

The project aims to determine the most effective EasyOCR approach for real-time car license plate recognition, focusing on speed, accuracy, and the architecture’s capacity to accurately record license plates. The vehicle license plate recognition system employs Easy Optical Character Recognition (EasyOCR) for real-time processing. The procedure commences with the input image, thereafter undergoing image preprocessing with the FuzzyWuzzy or SymSpell approach to improve image quality. Subsequently, license plate detection is executed to identify the license plate inside the image. The subsequent phase is license plate preprocessing, which aims to prepare the license plate for character processing. Afterwards, Easy Optical Character Recognition is executed to identify the characters on the observed license plate. Text formatting is executed to enhance the readability of character recognition outcomes, culminating in the system’s real-time output, which facilitates immediate and efficient car license plate recognition. The experimental findings indicate that the five OCR optimization techniques demonstrate varying efficacy in recognizing car license plates. FuzzyWuzzy and SymSpelL achieved optimal outcomes with recall and precision rates of 87.71% and 85.71%, respectively, alongside an accuracy of 85.72%. Both techniques are highly useful for real-time applications where rapidity and precision are essential. Local Binary Patterns (LBP) performed well, matching the recall and precision rates of 85.71% and an accuracy of 85.72%, similar to FuzzyWuzzy and SymSpelL, but it had a better confidence score of 70.71%, showing it is more reliable in detection.