Machine Learning based Number Plate Detection and Recognition

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.