APLICACIÓN DEL MODELO DE INTERSECCIÓN CORTICAL PARA SEGMENTAR CARACTERES EN UNA PLACA VEHICULAR (USING INTERSECTING CORTICAL MODEL FOR CHARACTER SEGMENTATION ON A LICENSE PLATE)

Elizabeth Xicotencatl Flores, Aldrin Barreto Flores, Salvador Eugenio Ayala Raggi, Verónica Edith Bautista López, José Francisco Portillo Robledo

Resumen


Resumen
Los sistemas de reconocimiento automático de placas vehiculares han sido mundialmente estudiados utilizando diversas técnicas de visión por computadora e inteligencia artificial. En este trabajo se presenta la aplicación de un modelo simplificado de una red neuronal pulso acoplada para separar los caracteres en una placa. Los pulsos óptimos para seleccionar la imagen binaria se determinan a partir del uso del valor de la entropía para descartar aquellos donde los caracteres no son visibles. A la imagen resultante se le aplica un análisis de regiones individuales para obtener sólo los objetos de interés y a partir de ello separar cada caracter alfanumérico. El algoritmo se evaluó en imágenes de placas vehiculares obtenidas en condiciones no controladas permitiendo tener resultados favorables en la mayoría de los casos.
Palabras Clave: Redes neuronales artificiales, segmentación de caracteres, reconocimiento de imágenes.

Abstract
Automatic license plate recognition systems have been studied worldwide using several computer vision and artificial intelligence techniques. In this work we present the application of a simplified model of a pulse coupled neural network to separate characters on a plate. The optimal pulses to select the binary image are determined using an entropy value, to discard those where the characters are not visible. Individual regions analysis is applied to the resulting image to obtain only the objects of interest and then separate each alphanumeric character. The algorithm was evaluated on images of license plates obtained under uncontrolled conditions, allowing favorable results in most of the cases.
Keywords: Artificial neural networks, character segmentation, image recognition.

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Referencias


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