Implementation of Automatic Vehicle License Plate Detection Using Python, Opencv and Tesseract OCR

Authors

  • Dr. C. Geetha  Associate Professor, Department of Electronics and Communication Engineering, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor Dist, Andhra Pradesh, India
  • Dr. M. Shantha Kumar  Associate Professor, Department of Electronics and Communication Engineering, Paavai Engineering College, Pachal, Namakkal Tamilnadu, India

Keywords:

OCR, Tessearact, Number plate, Vehicle, ALPR

Abstract

The rapid growth of vehicle populations necessitates efficient methods for automating tasks related to vehicle identification and surveillance. This paper presents a novel approach for automatic license plate detection from live input video streams using the OpenCV computer vision library and the Tesseract Optical Character Recognition (OCR) engine. The proposed system aims to enhance the accuracy and reliability of license plate recognition while catering to real-time processing requirements. The methodology involves a multi-step process. Initially, frames are captured from the live input video feed, and then preprocessed using OpenCV techniques such as resizing, noise reduction, and edge detection. Subsequently, region-of-interest (ROI) extraction is performed to isolate the candidate license plate regions within each frame. The extracted ROIs are further refined using contour analysis and geometric properties to improve the accuracy of license plate detection. Following the detection phase, the Tesseract OCR engine is employed to perform character recognition on the detected license plate regions. The system's architecture facilitates seamless integration between OpenCV and Tesseract, allowing for efficient data exchange and processing. The recognized characters are then validated using post-processing techniques to ensure accurate license plate number extraction. Experimental results on a diverse set of live input video scenarios demonstrate the effectiveness of the proposed system in accurately detecting and recognizing license plates in real time.

References

  1. Gonzalez et al. (2016): "Automatic License Plate Recognition Using Python and OpenCV".
  2. Pradhan et al. (2018): "License Plate Recognition System using OpenCV and Tesseract".
  3. Muhammad et al. (2020): "Real-time License Plate Detection and Recognition using Deep Learning and Tesseract OCR"
  4. Rathod et al. (2021): "An Improved License Plate Recognition System using OpenCV and Tesseract"
  5. Zheng et al. (2022): "Real-time License Plate Detection and Recognition System using Deep Learning and Tesseract"
  6. Kumar et al. (2017): "Real-time Automatic License Plate Recognition System using OpenCV"
  7. Oliveira et al. (2019): "License Plate Recognition System using OpenCV and Tesseract" .
  8. Smith et al. (2020): "Deep Learning-Based License Plate Detection and Recognition from Live Video Streams".
  9. Chen et al. (2021): "Real-time License Plate Detection and Recognition using OpenCV and Tesseract" .
  10. Gupta et al. (2022): "Hybrid License Plate Recognition System with Deep Learning and Tesseract OCR".

International Journal of Scientific Research in Science and Technology

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Published

2024-02-29

Issue

Volume 11, Issue 1, January-February-2024

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. C. Geetha, Dr. M. Shantha Kumar "Implementation of Automatic Vehicle License Plate Detection Using Python, Opencv and Tesseract OCR " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 11, Issue 1, pp.101-112, January-February-2024.