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  .
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 .
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combine deep learning and
address the challenge of extracting structured information from business documents without detailed annotations. We propose Deep
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CPCFG) to
DeepCPCFG) to parse two-dimensional complex documents and use Recursive Neural Networks to
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extracting structured information from complex documents. For each class of documents, we create a CPCFG that describes the structure of the
finding the most probable parse that represents the structured
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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.
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(Document, Record) pairs.
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.
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despite using no hand-annotations