A Vehicular License Plate Recognition Framework For Skewed Images

Abstract

Vehicular license plate (LP) recognition system has risen as a significant field of research recently because various explorations are currently being conducted by the researchers to cope with the challenges of LPs which include different illumination and angular situations. This research focused on restricted conditions such as using image of only one vehicle, stationary background, no angular adjustment of the skewed images. A real time vehicular LP recognition scheme is proposed for the skewed images for detection, segmentation and recognition of LP. In this research, a polar co-ordinate transformation procedure is implemented to adjust the skewed vehicular images. Besides that, window scanning procedure is utilized for the candidate localization that is based on the texture characteristics of the image. Then, connected component analysis (CCA) is implemented to the binary image for character segmentation where the pixels get connected in an eight-point neighbourhood process. Finally, optical character recognition is implemented for the recognition of the characters. For measuring the performance of this experiment, 300 skewed images of different illumination conditions with various tilt angles have been tested. The results show that proposed method able to achieve accuracy of 96.3% in localizing, 95.4% in segmenting and 94.2% in recognizing the LPs with an average localization time of 0.52s.

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

“…Several methods have been introduced for character segmentation. For instance, Arafat et al [9] proposed a license plate character segmentation method based on CCA. The detected license plate region was converted into a binary image and eight connected components were used for character region labeling.…”

“…Jin et al [3] 85.48 91.75 85.35 Arafat et al [9] 85.45 93.45 90.37 Samma et al [12] 80.35 -91.70 Tabrizi et al [13] 84.45 90.50 92.86 Niu et al [28] 85.80 -89.32 Li et al [45] --92.71 Thakur et al [37] 82…”

“…The rate of accuracy for these localization methods varies from 80.00% to 99.80% [3,6,7]. The methods most commonly used for the recognition stage are optical character recognition (OCR) [8,9], template matching [10,11], feature extraction and classification [12,13], and deep learning based methods [5,14].…”