DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction
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Abstract (arXiv)
We address the challenge of extracting structured information from business documents without detailed annotations. We propose Deep Conditional Probabilistic Context Free Grammars (DeepCPCFG) to parse two-dimensional complex documents and use Recursive Neural Networks to create an end-to-end system for finding the most probable parse that represents the structured information to be extracted. This system is trained end-to-end with scanned documents as input and only relational-records as labels. The relational-records are extracted from existing databases avoiding the cost of annotating documents by hand. We apply this approach to extract information from scanned invoices achieving state-of-the-art results despite using no hand-annotations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 10 Mar 2021.
Builds on
23 of 48 references.
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11 more
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Citing papers per year: 2021: 1; 2022: 2.
3 papers.