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

Autor:	Cooney, Ciaran, Cavadas, Joana, Madigan, Liam, Savage, Bradley, Heyburn, Rachel, O'Cuinn, Mairead
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose end-to-end document classification and key information extraction (KIE) for automating document processing in forms. Through accurate document classification we harness known information from templates to enhance KIE from forms. We use text and layout encoding with a cosine similarity measure to classify visually-similar documents. We then demonstrate a novel application of mixed integer programming by using assignment optimization to extract key information from documents. Our approach is validated on an in-house dataset of noisy scanned forms. The best performing document classification approach achieved 0.97 f1 score. A mean f1 score of 0.94 for the KIE task suggests there is significant potential in applying optimization techniques. Abation results show that the method relies on document preprocessing techniques to mitigate Type II errors and achieve optimal performance. Comment: 10 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.00750 Zobrazit plný text záznamu View this record from Arxiv