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.

TableSense 是一种端到端的框架，利用卷积神经网络的成功应用于计算机视觉中，采用有效的单元格特征化方案，增强的卷积神经网络模型和有效的不确定性指标来引导基于主动学习的智能采样算法，使训练数据集高效构建，有效提高电子表格表格检测的准确度。