Abstract
Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases.
Highlights
License plate recognition (LPR) system plays a key role in intelligent transportation systems, such as traffic control, parking lot access control, electronic toll collection, and information management
There is no restriction on the size of license plate (LP) characters to detect the LP, which can be observed from Figures 10(a23) and 10(b23)
There are very few methods in the literature, which talks about the LP detection of motorcycles, where LP characters fall in two lines and each line of characters are of different size
Summary
License plate recognition (LPR) system plays a key role in intelligent transportation systems, such as traffic control, parking lot access control, electronic toll collection, and information management. These four steps can be achieved by the combination of different techniques of image processing and pattern recognition Out of these four steps, the LP detection and character recognition steps are very crucial for the success of LPR systems. To assess the performance of the LP detection methods, there is a need for a common publicly available benchmark LP data set, which should contain videos and images taken in an open environment and with different plate variations. A common publicly available benchmark LP data set, for performance evaluation of LPR systems, which is initiated by Anagnostopoulos et al in paper [1] and contains 741 still images of Greek LPs with several open environmental conditions and different plate variations is present at [2]. We are proposing new methods for LP detection, noisy/missed character extraction, and LP characters rotation correction.
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