Machine Learning based Number Plate Detection and Recognition

Zuhaib Ahmed Shaikh, Umair Ali Khan, Muhammad Awais Rajput, Abdul Wahid Memon

2016

Abstract

Automatic Number Plate Detection and Recognition (ANPDR) has become of significant interest with the substantial increase in the number of vehicles all over the world. ANPDR is particularly important for automatic toll collection, traffic law enforcement, parking lot access control, and gate entry control, etc. Due to the known efficacy of image processing in this context, a number of ANPDR solutions have been proposed. However, these solutions are either limited in operations or work only under specific conditions and environments. In this paper, we propose a robust and computationally-efficient ANPDR system which uses Deformable Part Models (DPM) for extracting number plate features from training images, Structural Support Vector Machine (SSVM) for training a number plate detector with the extracted DPM features, several image enhancement operations on the extracted number plate, and Optical Character Recognition (OCR) for extracting the numbers from the plate. The results presented in this paper, obtained by long-term experiments performed under different conditions, demonstrate the efficiency of our system. They also show that our proposed system outperforms other ANPDR techniques not only in accuracy, but also in execution time.

Paper Citation

in Harvard Style

Shaikh Z., Khan U., Rajput M. and Memon A. (2016). Machine Learning based Number Plate Detection and Recognition . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 327-333. DOI: 10.5220/0005750203270333

in Bibtex Style

@conference{icpram16,
author={Zuhaib Ahmed Shaikh and Umair Ali Khan and Muhammad Awais Rajput and Abdul Wahid Memon},
title={Machine Learning based Number Plate Detection and Recognition},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={327-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005750203270333},
isbn={978-989-758-173-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Machine Learning based Number Plate Detection and Recognition
SN - 978-989-758-173-1
AU - Shaikh Z.
AU - Khan U.
AU - Rajput M.
AU - Memon A.
PY - 2016
SP - 327
EP - 333
DO - 10.5220/0005750203270333