Research Article DevelopmentofANPRFrameworkforPakistaniVehicleNumber Plates Using Object Detection and OCR Salma, Maham Saeed, Rauf ur Rahim, Muhammad Gufran Khan , Adil Zulfiqar, and Muhammad Tahir Bhatti Department of Electrical Engineering, National University of Computer and Emerging Science, Islamabad (Chiniot-Faisalabad Campus), Islamabad, Pakistan Correspondence should be addressed to Muhammad Gufran Khan; m.gufran@nu.edu.pk Received 12 February 2021; Accepted 27 September 2021; Published 19 October 2021 Academic Editor: Atila Bueno Copyright © 2021 Salma et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e metropolis of the future demands an efficient Automatic Number Plate Recognition (ANPR) system. Since every region has a distinct number plate format and style, an unconstrained ANPR system is still not available. ere is not much work done on Pakistani number plates because of the unavailability of the data and heterogeneous plate formations. Addressing this issue, we have collected a Pakistani vehicle dataset having various plate configurations and developed a novel ANPR framework using the dataset. e proposed framework localizes the number plate region using the YOLO (You Only Look Once) object detection model, applies robust preprocessing techniques on the extracted plate region, and finally recognizes the plate label using OCR (optical character recognition) Tesseract. e obtained mAP score of the YOLOv3 is 94.3% and the YOLOv4 model is 99.5% on the 0.50 threshold, whereas the average accuracy score of our framework is found to be 73%. For comparison and validation, we implemented a LeNet Convolutional Neural Network (CNN) architecture which uses the segmented image as an input. e comparative analysis shows that the proposed ANPR framework comprising the YOLOv4 and OCR Tesseract has good accuracy and inference time for a wide variation of illumination and style of Pakistani number plates and can be used to develop a real-time system. e proposed ANPR framework will be helpful for researchers developing ANPR for countries having similar challenging vehicle number plate formats and styles. 1.Introduction Vehicle ownership is increasing proportionally with the eco- nomic growth that makes the management and governance of the transportation system complicated. Violation of traffic rules, overspeeding, and car theft are common practices. e detection and retrieval of number plates from fast-moving vehicles make it hard to catch and penalize the culprit. e traffic congestion and unavailability of parking slots lead to the problem of time, fuel consumption, and air pollution. Till today, the vehicle number plate is often noted manually, and human errors in record keeping are unavoidable. Indeed, there is a need to have an automatic and efficient device for detecting, collecting, and managing car information. In the era of the fourth industrial revolution, an Intel- ligent Transport System (ITS) is a necessity in which the ability to share information without any human intervention is possible by the use of Artificial Intelligence (AI) and the Internet of ings (IoT). e most important subsystem of an ITS is Automatic Number Plate Recognition (ANPR). e ANPR system reads the image, preprocesses it, and recognizes the vehicle number plate characters independent of human involvement. It helps to identify potential risks, prevent crime, improve reliability, develop barrier-free in- frastructure, and provide location information. e Global Automatic Number Plate Recognition System Market is forecasted to increase with a ratio of 9.63% from 2017 to 2025 [1]. e studies show that existing ANPR methods are not viable, and it is hard to find a single efficient approach for different regions due to the unique format and style of each region. Several parameters, namely, vehicle pace, Hindawi Complexity Volume 2021, Article ID 5597337, 14 pages https://doi.org/10.1155/2021/5597337