Real Time License Plate Recognition from Video Streams using Deep Learning

Saquib Nadeem Hashmi, Kaushtubh Kumar, Siddhant Khandelwal, Dravit Lochan and Sangeeta Mittal
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Saquib Nadeem Hashmi: Jaypee Institute of Information Technology, Noida, India
Kaushtubh Kumar: Jaypee Institute of Information Technology, Noida, India
Siddhant Khandelwal: Jaypee Institute of Information Technology, Noida, India
Dravit Lochan: Jaypee Institute of Information Technology, Noida, India
Sangeeta Mittal: Jaypee Institute of Information Technology, Noida, India

International Journal of Information Retrieval Research (IJIRR), 2019, vol. 9, issue 1, 65-87

Abstract: With ever increasing number of vehicles, vehicular management is one of the major challenges faced by urban areas. Automation in terms of detecting vehicle license plate using real time automatic license plate recognition (RT-ALPR) approach can have many use cases in automated defaulter detection, car parking and toll management. It is a computationally complex task that has been addressed in this work using a deep learning approach. As compared to previous approaches, license plates have been recognized from full camera stills as well as parking videos with noise. On a dataset of 4800 car images, the accuracy obtained is 91% on number plate extraction from images, 93% on character recognition. Proposed ALPR system has also been applied to vehicle videos shot at parking exits. Overall 85% accuracy was obtained in real-time license number recognition from these videos.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jirr00:v:9:y:2019:i:1:p:65-87

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