TableSense: Spreadsheet Table Detection with Convolutional Neural Networks

TL;DR

TableSense is an innovative CNN-based framework that accurately detects spreadsheet tables by leveraging cell features, domain-specific boundary detection, and active learning, significantly outperforming existing methods.

Contribution

The paper introduces a novel end-to-end CNN framework with specialized cell features and active learning for efficient, accurate spreadsheet table detection.

Findings

Achieved 91.3% recall and 86.5% precision in table detection

Built a training dataset with 22,176 tables from 10,220 sheets

Significantly outperforms existing detection algorithms and CNN models

Abstract

Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for table detection to meet the domain-specific requirement on precise table boundary detection; third, we propose an effective uncertainty metric to guide an active learning based smart sampling algorithm, which enables the efficient build-up of a training dataset with 22,176 tables on 10,220 sheets with broad coverage of diverse table structures and layouts. Our evaluation shows that TableSense is highly effective with 91.3\% recall and 86.5\% precision in EoB-2 metric, a significant improvement over both the current detection algorithm that are used in commodity spreadsheet tools and state-of-the-art convolutional neural networks in computer vision.