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We propose end-to-end document classification and key information extraction(KIE) for automating document processing in forms. Through accurate documentclassification we harness known information from templates to enhance KIE fromforms. We use text and layout encoding with a cosine similarity measure toclassify visually-similar documents. We then demonstrate a novel application ofmixed integer programming by using assignment optimization to extract keyinformation from documents. Our approach is validated on an in-house dataset ofnoisy scanned forms. The best performing document classification approachachieved 0.97 f1 score. A mean f1 score of 0.94 for the KIE task suggests thereis significant potential in applying optimization techniques. Abation resultsshow that the method relies on document preprocessing techniques to mitigateType II errors and achieve optimal performance.

End-to-End Document Classification and Key Information Extraction using Assignment Optimization