Information Extraction from Visually Rich Documents with Font Style Embeddings

TL;DR

This paper introduces a novel approach for information extraction from native PDF documents by replacing visual embeddings with font style embeddings, resulting in improved efficiency and effectiveness.

Contribution

It demonstrates that using font style attribute embeddings instead of visual embeddings enhances model performance and reduces complexity in IE tasks on complex real-world datasets.

Findings

Font style embeddings improve F1-score by up to 2.29%.

Model complexity decreases by 30.7% with style embeddings.

Approach is effective on complex real-world datasets.

Abstract

Information extraction (IE) from documents is an intensive area of research with a large set of industrial applications. Current state-of-the-art methods focus on scanned documents with approaches combining computer vision, natural language processing and layout representation. We propose to challenge the usage of computer vision in the case where both token style and visual representation are available (i.e native PDF documents). Our experiments on three real-world complex datasets demonstrate that using token style attributes based embedding instead of a raw visual embedding in LayoutLM model is beneficial. Depending on the dataset, such an embedding yields an improvement of 0.18% to 2.29% in the weighted F1-score with a decrease of 30.7% in the final number of trainable parameters of the model, leading to an improvement in both efficiency and effectiveness.