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License Plate Detection and Location for Fast Character Segmentation
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Author(s): Juan Alberto Antonio Velázquez (Tecnológico de Estudios Superiores de Jocotitlán, Mexico), Leopoldo Gil Antonio (Tecnologico de Estudios Superiores de Jocotilán, Mexico), Ociel Felipe Gómez (Colegio de Estudios Científicos y Tecnológicos, Mexico), Hector Caballero Hernandez (Tecnologico de Estudios Superiores de Jocotilán, Mexico)and Erika López González (Tecnologico de Estudios Superiores de Jocotilán, Mexico)
Copyright: 2023
Pages: 15
Source title:
Handbook of Research on Applied Artificial Intelligence and Robotics for Government Processes
Source Author(s)/Editor(s): David Valle-Cruz (Universidad Autónoma del Estado de México, Mexico), Nely Plata-Cesar (Universidad Autónoma del Estado de México, Mexico)and Jacobo Leonardo González-Ruíz (Universidad Autónoma del Estado de México, Mexico)
DOI: 10.4018/978-1-6684-5624-8.ch016
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Abstract
In the State of Mexico, millions of vehicles circulate every day, so the identification of their license plates through CCTV cameras is an arduous job that requires trained personnel to capture important scenes. However, automatic plate recognition has factors that prevent correct detection. These can be problems such as weather, rusty plates, lighting, etc. A method of detecting plates located in any part of the vehicle is proposed and by means of a convolutional neural networks (CNN) algorithm using Tensor Flow. This is located, and then the text is segmented with the use of a threshold. The proposed model consists of a set of 355 individually obtained license plate images of the State of Mexico, which will serve to train the model. The main contribution of this work is the detection of vehicle plates anywhere, in addition to the implementation of a threshold with a value of 155 as the optimal value. The methodology was validated with the use of 50 new images of car plates registered in the vehicle registry of the State of Mexico, reaching an accuracy of 92.56% effectiveness.
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