A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

TL;DR

This paper introduces a saliency-based fully convolutional neural network combined with graphical models to accurately detect and localize tables and charts in digitized documents, improving over existing methods.

Contribution

It presents a novel approach that integrates saliency, multi-scale reasoning, and CRFs for enhanced detection of tables and charts in digital documents.

Findings

Outperforms existing models on ICDAR 2013 dataset

Effective localization of tables and charts in digitized documents

Demonstrates the benefit of combining saliency with deep learning

Abstract

Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in several application domains. Document analysis is a particularly promising area for DCNNs: indeed, the number of available digital documents has reached unprecedented levels, and humans are no longer able to discover and retrieve all the information contained in these documents without the help of automation. Under this scenario, DCNNs offers a viable solution to automate the information extraction process from digital documents. Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis carried out on an extended version of ICDAR 2013 (with annotated charts as well as tables) shows that our approach yields promising results, outperforming existing models.