TabIQA: Table Questions Answering on Business Document Images
Abstract
Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries. This paper introduces a novel pipeline, named TabIQA, to answer questions about business document images. TabIQA combines state-of-the-art deep learning techniques 1) to extract table content and structural information from images and 2) to answer various questions related to numerical data, text-based information, and complex queries from structured tables. The evaluation results on VQAonBD 2023 dataset demonstrate the effectiveness of TabIQA in achieving promising performance in answering table-related questions. The TabIQA repository is available at https://github.com/phucty/itabqa.
Cite
@article{arxiv.2303.14935,
title = {TabIQA: Table Questions Answering on Business Document Images},
author = {Phuc Nguyen and Nam Tuan Ly and Hideaki Takeda and Atsuhiro Takasu},
journal= {arXiv preprint arXiv:2303.14935},
year = {2023}
}
Comments
First two authors contributed equally