Handwritten Text Extraction and Digitization

Abstract

Extracting structured information from visually rich documents remains a complex task due to variations in layout, text alignment, and reading order. Traditional methods based on IOB tagging or graph decoding often struggle with irregular text sequences and the computational burden of large relational graphs. This paper introduces a novel anchor-based approach that redefines entity representation and association for structured information extraction. The proposed model, named Hwte, integrates visual and linguistic features through a multi-modal transformer architecture that jointly detects entities and their relationships. A new pre-training objective, Masked Detection Modelling (MDM), is introduced to enhance the model’s ability to predict both textual and spatial information simultaneously. Experimental evaluations on benchmark datasets demonstrate that the proposed method achieves superior accuracy and robustness compared to existing solutions, highlighting its effectiveness for real-world document understanding tasks.