Afghanistan Vehicle Number Plate Detection and Recognition Using Image Processing and Convolutional Neural Networks

Conference proceedings article

Authors/Editors

Strategic Research Themes

Publication Details

Author list: Hamdard, Javid; Krathu, Worarat;

Publisher: Elsevier

Publication year: 2021

Start page: 1

End page: 8

Number of pages: 8

ISBN: 9781450390125

ISSN: 0928-4931

eISSN: 1873-0191

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112154641&doi=10.1145%2f3468784.3469948&partnerID=40&md5=bc702d729f6c0c9828c73327f900707e

Languages: English-Great Britain (EN-GB)

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Abstract

Although numerous research studies have been conducted concerning automatic vehicle number plate detection and recognition, various presented automated number plate recognition systems are devised for specific countries where number plates follow standard patterns. However, such systems cannot be applied in Afghanistan because of the different designs and the language. Moreover, due to the cursive nature, writing direction, and shape variation of the Pashto characters, the segmentation of words into isolated characters is a more complicated task. Hence, the Pashto optical character recognition is a less developed area. To date, no research study has been conducted for Afghanistan number plate detection and recognition. The details on the Afghanistan number plate include character, numbers, and each province's name. The paper presents the study of its type attempting to detect the number plate from the vehicle image and then recognize the province's name, characters, and numbers on the number plate. In particular, the new method incorporating four core steps. The first step is number plate detection applying canny edge detection based on user-defined thresholding and extracts the number plate involving several image processing techniques. The second phase is number plate adjustment using Randon transform-based techniques. The third stage is number plate segmentation isolating each character, number, and province name on the number plate using a scanning approach. The final step employs a convolutional neural network to classify the number plate's alphanumeric characters and provinces' names. In addition, two datasets have been created: the dataset for alphanumeric characters contains 2800 images of 14 classes, and the dataset for provinces' names contains 6800 images of 34 classes. The proposed models present 99.93 percent accuracy for provinces' names classification and 98.93 percent for alphanumeric characters' classification. © 2021 ACM.

Keywords

Afghnistan Number Plate (NP) Detection, Pashto Language