  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.
