  Tabular data is a crucial form of information expression, which can organize
data in a standard structure for easy information retrieval and comparison.
However, in financial industry and many other fields tables are often disclosed
in unstructured digital files, e.g. Portable Document Format (PDF) and images,
which are difficult to be extracted directly. In this paper, to facilitate deep
learning based table extraction from unstructured digital files, we publish a
standard Chinese dataset named FinTab, which contains more than 1,600 financial
tables of diverse kinds and their corresponding structure representation in
JSON. In addition, we propose a novel graph-based convolutional neural network
model named GFTE as a baseline for future comparison. GFTE integrates image
feature, position feature and textual feature together for precise edge
prediction and reaches overall good results.
