Enhancing Document Key Information Localization Through Data Augmentation
This work addresses the problem of document key information localization for developers of document intelligence and understanding systems, and is an incremental improvement.
The authors tackled the problem of localizing key information in document images, achieving high performance in the VRDIU Track B competition by using a data augmentation approach. The result is an enhanced model generalization ability, although no specific numbers are provided.
The Visually Rich Form Document Intelligence and Understanding (VRDIU) Track B focuses on the localization of key information in document images. The goal is to develop a method capable of localizing objects in both digital and handwritten documents, using only digital documents for training. This paper presents a simple yet effective approach that includes a document augmentation phase and an object detection phase. Specifically, we augment the training set of digital documents by mimicking the appearance of handwritten documents. Our experiments demonstrate that this pipeline enhances the models' generalization ability and achieves high performance in the competition.