@inproceedings{zhang-etal-2024-unitabnet,
title = "{U}ni{T}ab{N}et: Bridging Vision and Language Models for Enhanced Table Structure Recognition",
author = "Zhang, Zhenrong and
Liu, Shuhang and
Hu, Pengfei and
Ma, Jiefeng and
Du, Jun and
Zhang, Jianshu and
Hu, Yu",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.355/",
doi = "10.18653/v1/2024.findings-emnlp.355",
pages = "6131--6143",
abstract = "In the digital era, table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. Previous methods primarily focus on visual aspects of table structure recovery but often fail to effectively comprehend the textual semantics within tables, particularly for descriptive textual cells. In this paper, we introduce UniTabNet, a novel framework for table structure parsing based on the image-to-text model. UniTabNet employs a {\textquotedblleft}divide-and-conquer{\textquotedblright} strategy, utilizing an image-to-text model to decouple table cells and integrating both physical and logical decoders to reconstruct the complete table structure. We further enhance our framework with the Vision Guider, which directs the model`s focus towards pertinent areas, thereby boosting prediction accuracy. Additionally, we introduce the Language Guider to refine the model`s capability to understand textual semantics in table images. Evaluated on prominent table structure datasets such as PubTabNet, PubTables1M, WTW, and iFLYTAB, UniTabNet achieves a new state-of-the-art performance, demonstrating the efficacy of our approach. The code will also be made publicly available."
}
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<abstract>In the digital era, table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. Previous methods primarily focus on visual aspects of table structure recovery but often fail to effectively comprehend the textual semantics within tables, particularly for descriptive textual cells. In this paper, we introduce UniTabNet, a novel framework for table structure parsing based on the image-to-text model. UniTabNet employs a “divide-and-conquer” strategy, utilizing an image-to-text model to decouple table cells and integrating both physical and logical decoders to reconstruct the complete table structure. We further enhance our framework with the Vision Guider, which directs the model‘s focus towards pertinent areas, thereby boosting prediction accuracy. Additionally, we introduce the Language Guider to refine the model‘s capability to understand textual semantics in table images. Evaluated on prominent table structure datasets such as PubTabNet, PubTables1M, WTW, and iFLYTAB, UniTabNet achieves a new state-of-the-art performance, demonstrating the efficacy of our approach. The code will also be made publicly available.</abstract>
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%0 Conference Proceedings
%T UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition
%A Zhang, Zhenrong
%A Liu, Shuhang
%A Hu, Pengfei
%A Ma, Jiefeng
%A Du, Jun
%A Zhang, Jianshu
%A Hu, Yu
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-unitabnet
%X In the digital era, table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. Previous methods primarily focus on visual aspects of table structure recovery but often fail to effectively comprehend the textual semantics within tables, particularly for descriptive textual cells. In this paper, we introduce UniTabNet, a novel framework for table structure parsing based on the image-to-text model. UniTabNet employs a “divide-and-conquer” strategy, utilizing an image-to-text model to decouple table cells and integrating both physical and logical decoders to reconstruct the complete table structure. We further enhance our framework with the Vision Guider, which directs the model‘s focus towards pertinent areas, thereby boosting prediction accuracy. Additionally, we introduce the Language Guider to refine the model‘s capability to understand textual semantics in table images. Evaluated on prominent table structure datasets such as PubTabNet, PubTables1M, WTW, and iFLYTAB, UniTabNet achieves a new state-of-the-art performance, demonstrating the efficacy of our approach. The code will also be made publicly available.
%R 10.18653/v1/2024.findings-emnlp.355
%U https://aclanthology.org/2024.findings-emnlp.355/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.355
%P 6131-6143
Markdown (Informal)
[UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition](https://aclanthology.org/2024.findings-emnlp.355/) (Zhang et al., Findings 2024)
ACL
- Zhenrong Zhang, Shuhang Liu, Pengfei Hu, Jiefeng Ma, Jun Du, Jianshu Zhang, and Yu Hu. 2024. UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6131–6143, Miami, Florida, USA. Association for Computational Linguistics.