Proceedings of the Multimedia University Engineering Conference (MECON 2022)

<Previous Article In Volume
Next Article In Volume>

An Evaluation of Various Pre-trained Optical Character Recognition Models for Complex License Plates

Authors
Haziq Idrose1, Nouar AlDahoul1, *, Hezerul Abdul Karim1, *, Rehan Shahid2, Manish Kumar Mishra2
1Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia
2Tapway Sdn Bhd, Petaling Jaya, Selangor, Malaysia
*Corresponding author. Email: nouar.aldahoul@live.iium.edu.my
*Corresponding author. Email: hezerul@mmu.edu.my
Corresponding Authors
Nouar AlDahoul, Hezerul Abdul Karim
Available Online 23 December 2022.
DOI
10.2991/978-94-6463-082-4_4How to use a DOI?
Keywords
Optical Character Recognition; License Plate Recognition; Pre-trained deep learning models; KerasOCR
Abstract

Optical Character Recognition (OCR) has been investigated widely to recognize characters in images for various applications including license plate recognition. Several limitations and distortions are available in images such as noise, blurring, and closed characters (alphabet and numbers) which makes the task of recognition more complex. This paper addresses the closed characters and blurring problem utilizing three pre-trained deep learning OCR models including Pytesseract, EasyOCR and KerasOCR. We evaluated and compared these methods using a dataset that contains Malaysian license plates. The results show that KerasOCR was able to outperform other methods in terms of recognition accuracy. KerasOCR was able to recognize 107 images out of 264 images compared to only 87 images in EasyOCR and 97 images in Pytesseract.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the Multimedia University Engineering Conference (MECON 2022)
Series
Advances in Engineering Research
Publication Date
23 December 2022
ISBN
10.2991/978-94-6463-082-4_4
ISSN
2352-5401
DOI
10.2991/978-94-6463-082-4_4How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Haziq Idrose
AU  - Nouar AlDahoul
AU  - Hezerul Abdul Karim
AU  - Rehan Shahid
AU  - Manish Kumar Mishra
PY  - 2022
DA  - 2022/12/23
TI  - An Evaluation of Various Pre-trained Optical Character Recognition Models for Complex License Plates
BT  - Proceedings of the Multimedia University Engineering Conference (MECON 2022)
PB  - Atlantis Press
SP  - 21
EP  - 27
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-082-4_4
DO  - 10.2991/978-94-6463-082-4_4
ID  - Idrose2022
ER  -