Enhancing Multimodal Information Extraction from Visually Rich Documents with 2D Positional Embeddings

Visually rich document understanding involves the interpretation of documents with varied formats and complex layouts, including multi-line entities, presenting a significant challenge. This study addresses these challenges by introducing a document comprehension model based on the LayoutLMv3 architecture, incorporating two-dimensional (2D) positional embeddings to capture both row and column information. Additionally, a multi-stage Transformer network is employed for hierarchical processing of document features. The proposed model is evaluated through extensive experiments on FUNSD dataset, achieving improved performance in both text-centric and image-centric document understanding tasks. Results demonstrate enhanced spatial comprehension and computational efficiency as compared to the state-of-the-art, establishing our approach as a significant contribution to the field of visually rich document understanding.