UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
This work addresses the challenge of parsing tables for machines, which is incremental as it builds on prior task-specific approaches by unifying training paradigms and objectives.
The paper tackles the problem of table recognition by proposing UniTable, a unified framework that uses self-supervised pretraining on pixel-level inputs to achieve state-of-the-art performance on four large datasets, surpassing existing methods and general vision-language models like GPT-4o.
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.