Detección de objetos en documentos digitales basado en algoritmos de Machine Learning

Abstract

This paper proposes a framework for creating trained models for object detection in digital documents using convolutional neural networks (CNNs). CNNs are artificial networks that are capable of learning at different levels of abstraction and are structured to resemble neurons in the primary visual cortex of a biological brain. The X101-FPN model, which is based on pre-trained neural networks exposed within the collections offered by the Detectron 2 library, is selected for this research. The model contains a hierarchical structure of 101 CNN layers, a residual neural network, and a pyramidal network of functions. This research presents a case study for the detection of QR codes using Artificial Intelligence (AI) techniques with convolutional neural networks, based on successful case studies using different Deep Learning techniques. The pre-trained model is trained on different types of preprocessed images and with various approaches to measuring correct accuracy and performance against proposed scenarios. Finally, various tests are performed to conclude the conditions and best versions of training for QR code detection.