Text extraction and recognition method for license plates

Abstract

Text extraction from images has always been challenging, especially if the image is taken under bad conditions, like lightning and noise that can influence text detection and recognition. This paper introduces a novel text extraction and recognition technique applied to the case study license plates. The main idea of this study is to detect the license plate in an input image and try to figure out the original country of the car based on the license plate. To accomplish this task, we first started collecting images from the internet, which were about 100 images. Afterward, we extracted the license plate using machine learning methods. Subsequently, we applied k-means clustering as well as thresholding in order to segment the extracted license plate and make the character recognition task easier. Thereafter, a sequence of techniques were applied, such as resizing and cropping the image to limit the wanted area of the desired character we want to extract. The last part of the proposed method is reading the text from the image using EasyOcr method, and using the function find in order to search for the character or the word. his proposed method achieved satisfactory results in detection where we achieved an accuracy of 87%, and a recognition of 97%. As for finding the ‘word’ part, the algorithm succeeded in all the examples.