Hiroshi Kawakami

Uma nova abordagem utilizando RNA para reconhecimento automático de placas de veículos baseado em pré-processamento de imagens e pós-processamento

This work presents a new system based on artificial neural networks (RNA) techniques to improve the automatic recognition of the number plate from degraded images. The input of the system are images automatically generated by photographic radars installed at the roads in Uberlândia city. The proposed system is composed by a degraded image preprocessing stage and a number plate code recognition stage. New preprocessing methods for image binarization, character segmentation and feature extraction are applied. The RNA model is trained with a set of output patterns that have low similarity between them. This model is used to recognize each character extracted from the number plate. The recognized characters sequence serves as the input data to the postprocessing stage that accepts or rejects that sequence as the result of the number plate recognition. Despite the degraded quality of the input images, experimental results have presented promising results. The output patterns used in the training stage of the RNA model and the proposed postprocessor have significantly increased the recognition rate showing the robustness of the system to process this kind of image.