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Table extraction from PDF and image documents is a ubiquitous task in thereal-world. Perfect extraction quality is difficult to achieve with one singleout-of-box model due to (1) the wide variety of table styles, (2) the lack oftraining data representing this variety and (3) the inherent ambiguity andsubjectivity of table definitions between end-users. Meanwhile, buildingcustomized models from scratch can be difficult due to the expensive nature ofannotating table data. We attempt to solve these challenges with TableLab byproviding a system where users and models seamlessly work together to quicklycustomize high-quality extraction models with a few labelled examples for theuser's document collection, which contains pages with tables. Given an inputdocument collection, TableLab first detects tables with similar structures(templates) by clustering embeddings from the extraction model. Documentcollections often contain tables created with a limited set of templates orsimilar structures. It then selects a few representative table examples alreadyextracted with a pre-trained base deep learning model. Via an easy-to-use userinterface, users provide feedback to these selections without necessarilyhaving to identify every single error. TableLab then applies such feedback tofinetune the pre-trained model and returns the results of the finetuned modelback to the user. The user can choose to repeat this process iteratively untilobtaining a customized model with satisfactory performance.

TableLab: An Interactive Table Extraction System with Adaptive Deep Learning