Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has mainly centered around complex task-specific combinations of available inputs and tools. We present UniTable, a training framework that unifies both the training paradigm and training objective of TR. Its training paradigm combines the simplicity of purely pixel-level inputs with the effectiveness and scalability empowered by self-supervised pretraining from diverse unannotated tabular images. Our framework unifies the training objectives of all three TR tasks - extracting table structure, cell content, and cell bounding box - into a unified task-agnostic training objective: language modeling. Extensive quantitative and qualitative analyses highlight UniTable's state-of-the-art (SOTA) performance on four of the largest TR datasets. UniTable's table parsing capability has surpassed both existing TR methods and general large vision-language models, e.g., GPT-4o, GPT-4-turbo with vision, and LLaVA. Our code is publicly available at https://github.com/poloclub/unitable, featuring a Jupyter Notebook that includes the complete inference pipeline, fine-tuned across multiple TR datasets, supporting all three TR tasks.

翻译：表格传递事实性和定量数据，其包含由人类创建的隐含规则，这些规则通常对机器解析构成挑战。先前关于表格识别（TR）的研究主要围绕可用输入和工具的复杂任务特定组合展开。我们提出了UniTable，一个统一TR训练范式与训练目标的训练框架。其训练范式结合了纯像素级输入的简洁性与自监督预训练从多样无标注表格图像中赋能的有效性和可扩展性。我们的框架将全部三个TR任务——提取表格结构、单元格内容及单元格边界框——的训练目标统一为一个任务无关的训练目标：语言建模。广泛的定量与定性分析突显了UniTable在四个最大TR数据集上的最先进（SOTA）性能。UniTable的表格解析能力已超越现有TR方法及通用大型视觉-语言模型，例如GPT-4o、具备视觉功能的GPT-4-turbo以及LLaVA。我们的代码公开于https://github.com/poloclub/unitable，包含一个Jupyter Notebook，其中提供了完整的推理流程，并在多个TR数据集上进行了微调，支持全部三个TR任务。