UniTable: Towards a Unified Framework for Table Structure Recognition
  via Self-Supervised Pretraining
ShengYun Peng Seongmin Lee Xiaojing Wang Rajarajeswari Balasubramaniyan Duen Horng Chau
Tables convey factual and quantitative data with implicit conventions created
by humans that are often challenging for machines to parse. Prior work on table
structure recognition (TSR) 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
TSR. Its training paradigm combines the simplicity of purely pixel-level inputs
with the effectiveness and scalability empowered by self-supervised pretraining
(SSP) from diverse unannotated tabular images. Our framework unifies the
training objectives of all three TSR tasks - extracting table structure, cell
content, and cell bounding box (bbox) - 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
TSR datasets. To promote reproducible research, enhance transparency, and SOTA
innovations, we open-source our code at https://github.com/poloclub/unitable
and release the first-of-its-kind Jupyter Notebook of the whole inference
pipeline, fine-tuned across multiple TSR datasets, supporting all three TSR
tasks.