Interactive Table Repair

with Jane Hoffswell

PDF documents often contain rich data tables that offer op-portunities for dynamic reuse in new interactive applications. We describe a pipeline for extracting, analyzing, and parsing PDF tables based on existing machine learning and rule-based techniques. Implementing and deploying this pipelineon a corpus of 447 documents with 1,171 tables results in only 11 tables that are correctly extracted and parsed. To improve the results of automatic table analysis, we first present a taxonomy of errors that arise in the analysis pipeline and discuss the implications of cascading errors on the user experience. We then contribute a system with two sets of lightweight interaction techniques (gesture and toolbar), for viewing andrepairing extraction errors in PDF tables on mobile devices. In an evaluation with 17 users involving both a phone and a tablet, participants effectively repaired common errors in 10 tables, with an average time of about 2 minutes per table.


Interactive Repair of Tables Extracted from PDF Documents on Mobile Devices
CHI 2019