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.

本文提出了一种基于端到端文档分类和关键信息提取的表格自动处理方法，其中采用了文本和布局编码技术，利用余弦相似度度量来对视觉上相似的文档进行分类，进而使用混合整数规划来提取文档中的关键信息，实验表明本方法对于文档预处理等优化技术具有重要作用，并取得了令人满意的实验效果。