通过并行系统提高准确性并加快文档图像分类

本文提出了一项研究，该研究显示了EfficientNet模型与较重的卷积神经网络（CNN）相比，在文档分类任务中的优势，这是机构数字化过程中的基本问题。我们在RVL-CDIP数据集中显示，我们可以使用更轻量的模型来改善以前的结果，并在较小的域内数据集（例如Tobacco3482）上展示其转移学习功能。..

Improving accuracy and speeding up Document Image Classification through parallel systems

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much lighter model and present its transfer learning capabilities on a smaller in-domain dataset such as Tobacco3482.Moreover, we present an ensemble pipeline which is able to boost solely image input by combining image model predictions with the ones generated by BERT model on extracted text by OCR. We also show that the batch size can be effectively increased without hindering its accuracy so that the training process can be sped up by parallelizing throughout multiple GPUs, decreasing the computational time needed. Lastly, we expose the training performance differences between PyTorch and Tensorflow Deep Learning frameworks.