Information Extraction from Visually Rich Documents with Font Style Embeddings
This work addresses information extraction from native PDF documents for industrial applications, offering incremental improvements in efficiency and effectiveness.
The paper tackled information extraction from visually rich documents by proposing to use token style attributes instead of raw visual embeddings in LayoutLM, resulting in improvements of 0.18% to 2.29% in weighted F1-score and a 30.7% reduction in trainable parameters across three datasets.
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.