  Table extraction from PDF and image documents is a ubiquitous task in the
real-world. Perfect extraction quality is difficult to achieve with one single
out-of-box model due to (1) the wide variety of table styles, (2) the lack of
training data representing this variety and (3) the inherent ambiguity and
subjectivity of table definitions between end-users. Meanwhile, building
customized models from scratch can be difficult due to the expensive nature of
annotating table data. We attempt to solve these challenges with TableLab by
providing a system where users and models seamlessly work together to quickly
customize high-quality extraction models with a few labelled examples for the
user's document collection, which contains pages with tables. Given an input
document collection, TableLab first detects tables with similar structures
(templates) by clustering embeddings from the extraction model. Document
collections often contain tables created with a limited set of templates or
similar structures. It then selects a few representative table examples already
extracted with a pre-trained base deep learning model. Via an easy-to-use user
interface, users provide feedback to these selections without necessarily
having to identify every single error. TableLab then applies such feedback to
finetune the pre-trained model and returns the results of the finetuned model
back to the user. The user can choose to repeat this process iteratively until
obtaining a customized model with satisfactory performance.
