A THESIS SUBMITTED TO SCHOOL OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE

Abstract

Digital Image Processing is application of computer algorithms to process, manipulate and interpret images. As a field it is playing an increasingly important role in many aspects of people’s daily life. Even though Image Processing has accomplished a great deal on its own, nowadays researches are being conducted in using it with Deep Learning (which is part of a broader family, Machine Learning) to achieve better performance in detecting and classifying objects in an image. Car’s License Plate Recognition is one of the hottest research topics in the domain of Image Processing (Computer Vision). It is having wide range of applications since license number is the primary and mandatory identifier of motor vehicles. When it comes to license plates in Ethiopia, they have unique features like Amharic characters, differing dimensions and plate formats. Although there is a research conducted on ELPR, it was attempted using the conventional image processing techniques but never with deep learning. In this research an attempt has been made in tackling the problem of recognizing Ethiopian license plates with better accuracy using both deep learning and image processing. Tensorflow was used in building the deep learning model and all the image processing is done with OpenCV-Python. So, the developed deep learning model was able to recognizes Ethiopian license plates with better accuracy by achieving 99.1%, 86.66% and 98% accuracy on plate detection, segmentation and recognition respectively which averages to an overall accuracy of 94.6%. Keywords: Amharic Characters, Deep Learning, Image Processing, License Plate, OpenCV-Python, Tensorflow