International Journal For Multidisciplinary Research
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Plagiarism is checked by the leading plagiarism checker
Volume 6 Issue 2
Automatic License Plate Recognition with CNN Method in Machine Learning
|Lokesh Jain, Geetika Vashisht
|In this period of consistently expanding innovation, there is a tremendous interest among individuals in a no-problem-at-all day-to-day way of life and travel. With the gigantic improvement in the vehicular area consistently, following individual cars has turned into an undeniably challenging task. With reconnaissance cameras on the side of the road, this thought proposes automatic license plate recognition for vehicles rushing. To tackle this issue, a proficient deep learning model called a Convolutional neural network is utilized for object identification and Simple OCR for character recognition. ALPR (Automatic License Plate Recognition), quite possibly one of the most broadly utilized computer vision applications is the subject of the proposed work. Furthermore, the paper provides insights into publicly accessible evaluation metrics and benchmark results, establishing a standardized framework for the quantitative assessment of automatic license plate recognition research employing CNN image resnet. The ultimate goal is to contribute to a deeper understanding of the latest state-of-the-art studies in automatic license plate recognition, particularly within the context of CNN image resnet in machine learning.
|Automatic License Plate Recognition, Deep Learning, CNN., Image Processing
|Volume 6, Issue 1, January-February 2024
|Automatic License Plate Recognition with CNN Method in Machine Learning - Lokesh Jain, Geetika Vashisht - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.11930
CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.