TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous

TL;DR

This paper introduces TableNet, a large-scale table dataset created using an LLM-powered autonomous system that synthesizes and recognizes diverse table images, enhancing table structure recognition research.

Contribution

It presents the first LLM-powered autonomous system for generating and recognizing diverse tables, enabling large-scale dataset creation and improved recognition performance.

Findings

Achieved competitive recognition performance with fewer training samples.

Generated a wide variety of semantically coherent tables with controllable parameters.

Utilized active learning to improve model training efficiency and accuracy.

Abstract

Table Structure Recognition (TSR) requires the logical reasoning ability of large language models (LLMs) to handle complex table layouts, but current datasets are limited in scale and quality, hindering effective use of this reasoning capacity. We thus present TableNet dataset, a new table structure recognition dataset collected and generated through multiple sources. Central to our approach is the first LLM-powered autonomous table generation and recognition multi-agent system that we developed. The generation part of our system integrates controllable visual, structural, and semantic parameters into the synthesis of table images. It facilitates the creation of a wide array of semantically coherent tables, adaptable to user-defined configurations along with annotations, thereby supporting large-scale and detailed dataset construction. This capability enables a comprehensive and nuanced table image annotation taxonomy, potentially advancing research in table-related domains. In contrast to traditional data collection methods, This approach facilitates the theoretically infinite, domain-agnostic, and style-flexible generation of table images, ensuring both efficiency and precision. The recognition part of our system is a diversity-based active learning paradigm that utilizes tables from multiple sources and selectively samples most informative data to finetune a model, achieving a competitive performance on TableNet test set while reducing training samples by a large margin compared with baselines, and a much higher performance on web-crawled real-world tables compared with models trained on predominant table datasets. To the best of our knowledge, this is the first work which employs active learning into the structure recognition of tables which is diverse in numbers of rows or columns, merged cells, cell contents, etc, which fits better for diversity-based active learning.