Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks


  • venkatanaresh m Dept. of ECE, Sree Vidyanikethan Enginerring College, Tirupati, A.P, India
  • V. Roshini Dept. of ECE, Sree Vidyanikethan Enginerring College, Tirupati, A.P, India
  • N. Harshitha Dept. of ECE, Sree Vidyanikethan Enginerring College, Tirupati, A.P, India


Intelligent Transportation system , Vehicle License Plate Recognition , Convolutional Neural Network , You Only Look Once (yolo), Optical Character Recognition


The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool.


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article IJEAP :  Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks




How to Cite

venkatanaresh m, V. . Roshini, and N. . Harshitha, “Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks”, International Journal of Engineering and Applied Physics, vol. 2, no. 3, pp. 535–543, Sep. 2022.