Classificação de documentos de identificação com redes neurais convolucionais

Abstract

Deep Learning has played an important role in data processing, with one of its main techniques being convolutional neural networks, which has the power to automatically learn features included in images in their layers. However, these deep artificial neural networks need a significant amount of images, for the proposed problem, already separated into categories to perform the training and validations of models that are not always available. In this context, in this work was constructed a dataset with images of Brazilian identification document of National Registry (RG) and National Qualification Card (CNH) separating them into some categories and, since they are documents with sensitive information, add a significant amount of images to obtain good results, it was a step that took time. For this reason, care was taken to preserve this sensitive information. This work presents some architectures of deep artificial neural networks to classify the images for different categories. Experiments were performed using a graphics processing unit (GPU) and using only the central processing unit (CPU). Results above 99% were obtained in some scenarios that were tested in the course of the work for the different proposed architectures.