• Seerapu Lavanya
• M. Naga Keerthi

It is common for vehicle license plate recognition systems to use neural network techniques to improve their computational ability. The process involves capturing an image of the license plate and then using image processing and neural network techniques at different stages to pre-process, filter, extract features, segment, and recognize the characters on the license plate. These techniques are used to remove noise from the image, improve image quality, and convert the characters into text for further processing. A sample experiment was carried out using MATLAB to demonstrate the basic image processing techniques used for license plates, with a case study based on the license plate system. An algorithm was developed to create a solution for a parking management system, which was then implemented as proof of concept. Overall, the use of neural network techniques in license plate recognition systems has been successful in improving accuracy and efficiency, making these systems a valuable tool for various applications such as law enforcement, parking management, and toll collection.

Image processing, pre-processing, filtering, feature extraction, segmentation, recognition, experiment

"Licence Plate Recognition And Detection By Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.f237-f243, April-2023, Available :http://www.jetir.org/papers/JETIR2304930.pdf

"Licence Plate Recognition And Detection By Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppf237-f243, April-2023, Available at : http://www.jetir.org/papers/JETIR2304930.pdf