An Intelligent Video Based Automatic License Plate Detection and Analysis Using Easy OCR and Tensorflow

People want to live and travel more securely now that technology is progressing so quickly. In recent years, road traffic has increased. As the automotive business evolves constantly, keeping track of individual cars gets difficult. This research created an automated vehicle monitoring platform. The platform monitors speeding cars with roadside cameras. Obtaining real-time footage from background CCTV cameras may be tricky. Planned work has four stages. First, the automobile is recognised from each frame of video footage. Next, look for license plates on recognised cars. In the last phase, the observed number plates' features are recognised. The offered training approach uses Image AI to aid development. Odisha license plates are used to evaluate the model's accuracy. This paper develops and tests a Python prototype of a constraint-based real-time license plate recognition system. Plate localisation filters non-text portions based on the plate's vertical edges. Character documentation uses an MLP-based Optical Character Recognition (OCR) module. During recognition, license plate characters are compared to all potential varieties. The method analyses Indian and international license plate alignments, picture quality, and image size using photos. System includes plate alignments. 92% of the time for license plate location and 88% for character segmentation and recognition. The work presented shows 99% precision improvement and 93% accuracy improvement compared to other methods.

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Publication Details

Published
2025-07-25
DOI
https://doi.org/10.1109/isac364032.2025.11156342
Citations
1
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
1.14
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An Intelligent Video Based Automatic License Plate Detection and Analysis Using Easy OCR and Tensorflow

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1 citations
Vehicle License Plate Recognition
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article

An Intelligent Video Based Automatic License Plate Detection and Analysis Using Easy OCR and Tensorflow

Rohan Roshan Nayak, Laxmipriya Samal, Arun Agarwal, Debashisa Samal, Saurabh Mehta
article en
1 citations

Abstract

People want to live and travel more securely now that technology is progressing so quickly. In recent years, road traffic has increased. As the automotive business evolves constantly, keeping track of individual cars gets difficult. This research created an automated vehicle monitoring platform. The platform monitors speeding cars with roadside cameras. Obtaining real-time footage from background CCTV cameras may be tricky. Planned work has four stages. First, the automobile is recognised from each frame of video footage. Next, look for license plates on recognised cars. In the last phase, the observed number plates' features are recognised. The offered training approach uses Image AI to aid development. Odisha license plates are used to evaluate the model's accuracy. This paper develops and tests a Python prototype of a constraint-based real-time license plate recognition system. Plate localisation filters non-text portions based on the plate's vertical edges. Character documentation uses an MLP-based Optical Character Recognition (OCR) module. During recognition, license plate characters are compared to all potential varieties. The method analyses Indian and international license plate alignments, picture quality, and image size using photos. System includes plate alignments. 92% of the time for license plate location and 88% for character segmentation and recognition. The work presented shows 99% precision improvement and 93% accuracy improvement compared to other methods.

University of Mumbai (IN), Siksha O Anusandhan University (IN)
Openalex Percentile: Top 16%
Vehicle License Plate Recognition
1.14
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