UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 12 | Issue 10 | October 2025

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 10 Issue 12
December-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2312239


Registration ID:
529928

Page Number

c131-c136

Share This Article


Jetir RMS

Title

Indian Vehicle License Plate Extraction and Recognition Using Morphological Operations and Deep Learning

Abstract

License Plate Extraction and Recognition (LPER) play a vital role in various applications within intelligent transport systems (ITS), including surveillance, traffic flow monitoring, stolen vehicle tracking, and parking lot management. This paper presents a comprehensive system design for implementing LPER specifically tailored to Indian License Plates. The LPER process encompasses three essential stages: License Plate Extraction, segmentation, and classification. Each stage necessitates specialized techniques adapted to real-world scenarios, each characterized by distinct attributes. The license plate extraction techniques are utilized to accurately locate the license plate region, which is succeeded by segmentation algorithms that effectively isolate individual characters. Subsequently, a classification step is employed to recognize these segmented characters. The overall efficacy of the process hinges upon the precision achieved at each sequential step. To significantly enhance the classification step's performance, we propose an innovative amalgamated approach that integrates segmentation and classification into a cohesive single-stage process. This integration harnesses the power of deep learning methodologies, incorporating Morphological and Enhanced sliding contract window (ESCW)-based number plate detection alongside the employment of the AlexNet neural network. Our experimental findings underscore the efficacy of this proposed approach, showcasing an outstanding accuracy of 97.2% in accurately recognizing characters on vehicle license plates. This achievement notably surpasses the accomplishments of prior endeavors in this field, signifying a significant advancement in LPER technology.

Key Words

deep learning neural network, License plate extraction and recognition(LPER)

Cite This Article

"Indian Vehicle License Plate Extraction and Recognition Using Morphological Operations and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.c131-c136, December-2023, Available :http://www.jetir.org/papers/JETIR2312239.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Indian Vehicle License Plate Extraction and Recognition Using Morphological Operations and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppc131-c136, December-2023, Available at : http://www.jetir.org/papers/JETIR2312239.pdf

Publication Details

Published Paper ID: JETIR2312239
Registration ID: 529928
Published In: Volume 10 | Issue 12 | Year December-2023
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.37079
Page No: c131-c136
Country: Osmanabad, Maharastra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

000205

Print This Page

Current Call For Paper

Jetir RMS