Fast CNN-Based Document Layout Analysis

Abstract

Automatic document layout analysis is a crucial step in cognitive computing and processes that extract information out of document images, such as specific-domain knowledge database creation, graphs and images understanding, extraction of structured data from tables, and others. Even with the progress observed in this field in the last years, challenges are still open and range from accurately detecting content boxes to classifying them into semantically meaningful classes. 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 bidimensional CNN approach.