A Fast and Noise Tolerable Binarization Method for Automatic License Plate Recognition in the Open Environment in Taiwan

Abstract

License plate recognition is widely used in our daily life. Image binarization, which is a process to convert an image to white and black, is an important step of license plate recognition. Among the proposed binarization methods, Otsu method is the most famous and commonly used one in a license plate recognition system since it is the fastest and can reach a comparable recognition accuracy. The main disadvantage of Otsu method is that it is sensitive to luminance effect and noise, and this property is impractical since most vehicle images are captured in an open environment. In this paper, we propose a system to improve the performance of automatic license plates reorganization in the open environment in Taiwan. Our system uses a binarization method which is inspired by the symmetry principles. Experimental results showed that when our method has a similar time complexity to that of Otsu, our method can improve the recognition rate up to 1.30 times better than Otsu.

Performance Analysis on Automatic Number Plate Recognition System and Methods

“…The quick advancement of license plate recognition technology is used by the author is used in wide area in Intelligent Transportation System [12]. The attribute extraction model and the BPNN Backpropagation Neural Network are used in this methodology for license plate identification and character algorithm.…”

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

“…Ha and Vajgl [13] used a method in which the detected character is compared to an embedded database of templates until a match occurs, and they also used template techniques. Chun-Cheng Peng et al [17] used a binarization method, which was inspired by the symmetry principle, rather than the outs algorithm. Qadri and Asif [15] divided the image into regions, and then the regions were split into segments.…”