Papers › Improving accuracy and speeding up Document Image Classification through parallel systems

Improving accuracy and speeding up Document Image Classification through parallel systems

16 Jun 2020arXiv:2006.09141archive 2025-07-28

Javier Ferrando, Juan Luis Dominguez, Jordi Torres, Raul Garcia, David Garcia, Daniel Garrido, Jordi Cortada, Mateo Valero

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.

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Code

javiferran/document-classification pytorch report

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Tasks

Document Classification Document Image Classification General Classification Image Classification Multi-Modal Document Classification Optical Character Recognition (OCR)Transfer Learning document-image-classification image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Document Image Classification	RVL-CDIP	Pre-trained EfficientNet	Accuracy	92.31%	#23 of 31	Archive leaderboard	report

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Methods

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