DSG: An End-to-End Document Structure Generator
Johannes Rausch Gentiana Rashiti Maxim Gusev Ce Zhang Stefan Feuerriegel
Information in industry, research, and the public sector is widely stored as
rendered documents (e.g., PDF files, scans). Hence, to enable downstream tasks,
systems are needed that map rendered documents onto a structured hierarchical
format. However, existing systems for this task are limited by heuristics and
are not end-to-end trainable. In this work, we introduce the Document Structure
Generator (DSG), a novel system for document parsing that is fully end-to-end
trainable. DSG combines a deep neural network for parsing (i) entities in
documents (e.g., figures, text blocks, headers, etc.) and (ii) relations that
capture the sequence and nested structure between entities. Unlike existing
systems that rely on heuristics, our DSG is trained end-to-end, making it
effective and flexible for real-world applications. We further contribute a
new, large-scale dataset called E-Periodica comprising real-world magazines
with complex document structures for evaluation. Our results demonstrate that
our DSG outperforms commercial OCR tools and, on top of that, achieves
state-of-the-art performance. To the best of our knowledge, our DSG system is
the first end-to-end trainable system for hierarchical document parsing.