DocParser: Hierarchical Document Structure Parsing from Renderings

Translating renderings (e. g. PDFs, scans) into hierarchical document structures is extensively demanded in the daily routines of many real-world applications. However, a holistic, principled approach to inferring the complete hierarchical structure of documents is missing. As a remedy, we developed “DocParser”: an end-to-end system for parsing the complete document structure – including all text elements, nested figures, tables, and table cell structures. Our second contribution is to provide a dataset for evaluating hierarchical document structure parsing. Our third contribution is to propose a scalable learning framework for settings where domain-specific data are scarce, which we address by a novel approach to weak supervision that significantly improves the document structure parsing performance. Our experiments confirm the effectiveness of our proposed weak supervision: Compared to the baseline without weak supervision, it improves the mean average precision for detecting document entities by 39. 1 % and improves the F1 score of classifying hierarchical relations by 35. 8 %.

Authors

• Johannes Rausch
• Octavio Martinez
• Fabian Bissig
• Ce Zhang Carnegie Mellon University
• Stefan Feuerriegel LMU Munich Munich Center for Machine Learning

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