A Question Answering System for Unstructured Table Images

ABSTRACTQuestion answering over tables is a very popular semantic parsing task in natural language processing (NLP). However, few existing methods focus on table images, even though there are usually large-scale unstructured tables in practice (e.g., table images). Table parsing from images is nontrivial since it is closely related to not only NLP but also computer vision (CV) to parse the tabular structure from an image. In this demo, we present a question answering system for unstructured table images. The proposed system mainly consists of 1) a table recognizer to recognize the tabular structure from an image and 2) a table parser to generate the answer to a natural language question over the table. In addition, to train the model, we further provide table images and structure annotations for two widely used semantic parsing datasets. Specifically, the test set is used for this demo, from where the users can either choose from default questions or enter a new custom question.