Automatic Detection of Number Plate from Images of Bangladeshi Vehicles

3-5 April, 2017| Category: Conference| Authors: MNH Pias, AK Mutasim, MA Amin
In the 2017 International Conference on Advances in Artificial Intelligence (ICAAI 2017), Bangkok, Thailand

Abstract

In this paper, we propose a method to detect number plates of vehicles registered in Bangladesh. Our approach was to pre-train a deep Convolutional Neural Network with CIFAR-10 data, then fine tune the network by training it further with our dataset to create the Regions with Convolutional Neural Network (R-CNN) object detector. For training the R-CNN Region of Interest (ROI) labelled data was required. We have observed that using training data with a bigger ROI, which encapsulates the entire number plate within, enables the R-CNN to detect number plates more accurately. The proposed method can detect number plates with more than 99% accuracy.

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