Abstract
License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.
Highlights
Automatic license plate recognition has widespread applications in overcoming traffic violations, parking offenses, and better decision making for e-ticketing of vehicles [1].A license plate is a crucial token issued by the state authority for vehicle identification and record keeping
This paper presents a deep architecture to localize and recognize Pakistani license plates
Hands-on feature engineering methods: Notable works on Pakistani license plate recognition based on hands-on feature engineering include Malik et al [20] who used connected component analysis (CCA) to localize and recognize standard number plates of the Punjab province, which contain an inherent green region
Summary
Automatic license plate recognition has widespread applications in overcoming traffic violations, parking offenses, and better decision making for e-ticketing of vehicles [1]. Modern traffic management systems rely heavily on automatic monitoring systems based on computer vision and machine learning techniques These typically require a license plate of standard size, color, font, style, and fixed location for automated localization and recognition. In addition to a license plate, people use different text to mention their profession, tribe, political affiliation, etc. In such situations, the traditional license plate localization and recognition techniques fail to work [2]. This paper presents a deep architecture to localize and recognize Pakistani license plates. Our model correctly localizes and recognizes the license plate when there are other texts and handles the color, illumination, size, style, and font variations. The rest of the paper is organized as follows: the related work is discussed in Section 2, while Sections 3–5 present the proposed model, experimental details, and conclusion, respectively
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