Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Enhancing automatic license plate recognition in Indian scenarios

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 1 (2025)

DOI
10.11591/ijece.v15i1.pp365-373
ISSN
2722-2578, 2088-8708
Published
2025-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.

Collaboration network of the authors

10 authors · 27 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • R.V. College of Engineering
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
G. ShobhaR.V. College of Engineering1
Rahul M. PatilR.V. College of Engineering1
K. P. ChethanR.V. College of Engineering1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Enhancing automatic license plate recognition in Indian scenarios</dc:title><dc:creator>Samaga, Abhinav</dc:creator><dc:creator>Lobo, Allen Joel</dc:creator><dc:creator>Nasreen, Azra</dc:creator><dc:creator>Pattar, Ramakanth Kumar</dc:creator><dc:creator>Trivedi, Neeta</dc:creator><dc:creator>Raj, Peehu</dc:creator><dc:creator>Sreelakshmi, Koratagere</dc:creator><dc:description>&lt;jats:p&gt;Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v15i1.pp365-373</dc:identifier><dc:identifier>10.11591/ijece.v15i1.pp365-373</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 1 (2025); 365</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/36187/17940</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>