DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction

• URL: http://arxiv.org/abs/2103.05908v1
• Date: Wed, 10 Mar 2021 07:35:21 GMT
• Title: DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction
• Authors: Freddy C. Chua, Nigel P. Duffy
• Abstract summary: We combine deep learning and Conditional Probabilistic Context Free Grammars ( CPCFG) to create an end-to-end system for extracting structured information. We apply this approach to extract information from scanned invoices achieving state-of-the-art results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We combine deep learning and Conditional Probabilistic Context Free Grammars (CPCFG) to create an end-to-end system for extracting structured information from complex documents. For each class of documents, we create a CPCFG that describes the structure of the information to be extracted. Conditional probabilities are modeled by deep neural networks. We use this grammar to parse 2-D documents to directly produce structured records containing the extracted information. This system is trained end-to-end with (Document, Record) pairs. We apply this approach to extract information from scanned invoices achieving state-of-the-art results.

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