Fast CNN-Based Document Layout Analysis

Dario Augusto Borges Oliveira, Matheus Palhares Viana; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1173-1180

Abstract


Automatic document layout analysis is a crucial step in cognitive computing and processes that extract information out of document images. With the popularization of mobile devices and cloud-based services, the need for approaches that are both fast and economic in data usage is a reality. In this paper we propose a fast one-dimensional approach for automatic document layout analysis considering text, figures and tables based on convolutional neural networks (CNN). We take advantage of the inherently one-dimensional pattern observed in text and table blocks to reduce the dimension analysis from bi-dimensional documents images to 1D signatures, improving significantly the overall performance: we present considerably faster execution times and more compact data usage with no loss in overall accuracy if compared with a classical bi-dimensional CNN approach.

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[bibtex]
@InProceedings{Oliveira_2017_ICCV,
author = {Augusto Borges Oliveira, Dario and Palhares Viana, Matheus},
title = {Fast CNN-Based Document Layout Analysis},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}