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  Published Paper Details:

  Paper Title

VEHICLE NUMBER PLATE DETECTION AND RECOGNITION USING DEEP LEARNING

  Authors

  Dharmendrakumar Patel,  Ajay Maraviya,  Mohitrajsinh Parmar,  Parin Vaghani,  Dhawni Dave

  Keywords

Data Processing, Number Plate Detection, Character Recognition

  Abstract


Automatic License Plate recognition (ALPR) is an image processing technology which uses a number (license) plate to find the vehicle. The objective is to design an efficient authorized vehicle identification system by using the vehicle number/license plate. The system is implemented on the entrance for security control of a highly restricted area like military zones or areas around top government offices e.g., Parliament, Supreme Court etc. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for the character recognition. The resulting data is then used to compare with the records on a database to produce the specific information like the vehicle owner, place of registration, address, etc. The system and its performance are tested on real images. It is seen from the experiment that the developed system successfully detects and recognizes the vehicle number plate on real images.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2202061

  Paper ID - 215437

  Page Number(s) - a470-a473

  Pubished in - Volume 10 | Issue 2 | February 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Dharmendrakumar Patel,  Ajay Maraviya,  Mohitrajsinh Parmar,  Parin Vaghani,  Dhawni Dave,   "VEHICLE NUMBER PLATE DETECTION AND RECOGNITION USING DEEP LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 2, pp.a470-a473, February 2022, Available at :http://www.ijcrt.org/papers/IJCRT2202061.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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