TabIQA: Table Questions Answering on Business Document Images
This addresses the challenge of automated question answering from business document images for users in fields like finance or data analysis, but it appears incremental as it combines existing deep learning techniques.
The paper tackles the problem of answering questions from table images in business documents, which involves understanding tabular structures and performing numeric computations, and introduces TabIQA, a pipeline that achieves promising performance on the VQAonBD 2023 dataset.
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.