TabIQA: Table Questions Answering on Business Document Images

• URL: http://arxiv.org/abs/2303.14935v1
• Date: Mon, 27 Mar 2023 06:31:21 GMT
• Title: TabIQA: Table Questions Answering on Business Document Images
• Authors: Phuc Nguyen, Nam Tuan Ly, Hideaki Takeda, and Atsuhiro Takasu
• Abstract summary: 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.
• Score: 3.9993134366218857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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