Towards Automatic License Plate Recognition in Challenging Conditions
Source abstract
Abstract
License plate recognition (LPR) is an integral part of the current intelligent systems that are developed to locate and identify various objects. Unfortunately, the LPR is a challenging task due to various factors, such as the numerous shapes and designs of the LPs, the non-following of standard LP templates, irregular outlines, angle variations, and occlusion. These factors drastically influence the LP appearance and significantly challenge the detection and recognition abilities of state-of-the-art detection and recognition algorithms. However, recent rising trends in the development of machine learning algorithms have yielded encouraging solutions. This paper presents a novel LPR method to address the aforesaid issues. The proposed method is composed of three distinct but interconnected steps. First, a vehicle that appears in an input image is detected using the Faster RCNN. Next, the LP area is located within the detected vehicle by using morphological operations. Finally, license plate recognition is accomplished using the deep learning network. Detailed simulations performed on the PKU, AOLP, and CCPD databases indicate that our developed approach produces mean license plate recognition accuracy of 99%, 96.0231%, and 98.7000% on the aforesaid databases.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle License Plate Recognition · Handwritten Text Recognition Techniques · Smart Parking Systems Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Software Development, Axispoint Technology Solutions Group (ATSG), New York, NY 10119, USA
Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23460, Pakistan
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
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