Automatic Number Plate Recognition

Authors

Shaik Nehar, Vasireddy venkatadri institute of technology Nambur,guntur,522007.Follow
Sai vignesh chintala, Vasireddy venkatadri institute of technology Nambur,guntur,522007.Follow
Sanagala Teja, Vasireddy venkatadri institute of technology Nambur,guntur,522007.Follow
SrinivasaRao Tummalapalli, Vasireddy venkatadri institute of technology Nambur,guntur,522007.Follow
Lohith vattikuti, Vasireddy venkatadri institute of technology Nambur,guntur,522007.Follow
Mahendra Eda, Vasireddy venkatadri institute of technology Nambur,guntur,522007.Follow

Abstract

This innovative and comprehensive approach harnesses the combined power of YOLOv8 and EasyOCR technologies for Automatic Number Plate Recognition (ANPR). The primary objective is to extract valuable data from vehicle images, even when they are blurred or distorted, and utilize this data for further analysis using machine learning models.The cornerstone of this approach is the integration of two cuttingedge technologies: You Only Look Once (YOLO) V8 for Region of Interest (ROI) detection and EasyOCR for optical character recognition. The workflow begins with the utilization of YOLOv8, which excels in identifying and correcting distorted license plates within a single image. Its initial task is to pinpoint the Region of Interest (ROI). Once the ROI is identified, a series of preprocessing steps are employed to enhance the image quality before feeding it into the EasyOCR model for character recognition.Several significant advantages distinguish this approach. Firstly, it possesses the capability to deblur images, enabling accurate data extraction even from blurred or challenging images. Secondly, it synergizes the strengths of YOLOv8 for precise ROI detection and EasyOCR for accurate character recognition, resulting in a robust and dependable ANPR system. Lastly, the system is optimized for real-time processing, rendering it suitable for applications requiring swift and efficient vehicle monitoring.The key innovation lies in the ability to extend its functionality to video data, allowing for the detection of number plates in dynamic settings. It not only detects license plates but also provides an annotated video with comprehensive license plate details. This holistic approach facilitates a more complete understanding of vehicular movements and activities.By harnessing the capabilities of YOLOv8 and EasyOCR technologies, this approach delivers a comprehensive and sophisticated solution for Automatic Number Plate Recognition. It enhances accuracy, efficiency, and the ability to handle challenging scenarios, such as blurred images and dynamic video feeds. This innovative system serves as a valuable tool for promoting safe, secure, and modern transportation systems.

Recommended Citation

Nehar, Shaik; chintala, Sai vignesh; Teja, Sanagala; Tummalapalli, SrinivasaRao; vattikuti, Lohith; and Eda, Mahendra (2024) "Automatic Number Plate Recognition," American Journal of Electronics & Communication (AJEC): Vol. 4: Iss. 3, Article 4.
Available at: https://research.smartsociety.org/ajec/vol4/iss3/4