Reconocimiento óptico de caracteres para el reconocimiento de placas vehiculares
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Fecha
2020
Autores
Barragán, Yeisson
Barroso, Brayan
Peña, Johandri
Sinning, Brainer
Título de la revista
ISSN de la revista
Título del volumen
Editor
Ediciones Universidad Simón Bolívar
Facultad de Ingenierías
Facultad de Ingenierías
Resumen
En la actualidad, la inteligencia artificial tiene muchos campos en los que trabaja. Se ha convertido
en un pilar importante de la informática ya que ha beneficiado la vida humana en varios aspectos,
desde la medicina hasta juegos de computadoras.
Cuando se detecta una imagen tomada con contenido textual, muchas veces se busca la forma de
extraer dicho texto de forma automática, por lo que un campo de la inteligencia artificial nos
brinda la oportunidad de tener un aprendizaje profundo en el reconocimiento de textos. El
Reconocimiento Óptico de Caracteres (OCR) y la visión computacional son de gran ayuda para la
interpretación de los textos en una imagen, el cual contribuye en gran medida al dejar atrás las
lecturas manuales por parte de una persona y sean lecturas automáticas hechas por software.
Por tanto, este documento ofrece de forma general una propuesta de investigación encaminada a
la detección y lectura de imágenes que contienen placas vehiculares, la cual optimizará el
rendimiento de dicho proceso. El reconocimiento de las placas vehiculares tiene como fin mejorar
la forma en cómo se hacen las lecturas de las mismas en las imágenes tomadas por foto multas, ya
que benefician en gran medida a dicha empresa encargada del manejo de este proceso.
Currently, artificial intelligence has many fields in which it works. It has become an important pillar of computing as it has benefited human life in various aspects, from medicine to computer games. When an image taken with textual content is detected, many times it is desired to extract that text automatically, so a field of artificial intelligence gives us the opportunity to have a deep learning in text recognition. Optical Character Recognition (OCR) and computer vision are of great help for the interpretation of texts in an image, which contributes greatly to leaving behind manual readings by a person and using automatic readings made by software instead. Therefore, this document offers a general research proposal aimed at the detection and reading of images containing number plates, which will optimize the performance of this process. The recognition of vehicle plates is intended to improve the way in which the readings of number plates are made in the images taken by photo fines, since they benefit in great measure the company in charge of handling this process.
Currently, artificial intelligence has many fields in which it works. It has become an important pillar of computing as it has benefited human life in various aspects, from medicine to computer games. When an image taken with textual content is detected, many times it is desired to extract that text automatically, so a field of artificial intelligence gives us the opportunity to have a deep learning in text recognition. Optical Character Recognition (OCR) and computer vision are of great help for the interpretation of texts in an image, which contributes greatly to leaving behind manual readings by a person and using automatic readings made by software instead. Therefore, this document offers a general research proposal aimed at the detection and reading of images containing number plates, which will optimize the performance of this process. The recognition of vehicle plates is intended to improve the way in which the readings of number plates are made in the images taken by photo fines, since they benefit in great measure the company in charge of handling this process.
Descripción
Palabras clave
OCR, Reconocimiento de placas, Visión Computacional, Redes neuronales, Matricula, OCR, Plate Recognition, Computer vision, Neural networks, License Plate
Citación
[N]Y. Barragán*, B. Barroso*, J. Piña*, B. Sinning & S. Moreno-Trillos, “Reconocimiento Óptico de Caracteres para el reconocimiento de placas vehiculares”, Investigación y Desarrollo en TIC, vol. 7, no. 2, pp. 55-60 2016.
