  Information Extraction from visual documents enables convenient and
intelligent assistance to end users. We present a Neighborhood-based
Information Extraction (NIE) approach that uses contextual language models and
pays attention to the local neighborhood context in the visual documents to
improve information extraction accuracy. We collect two different visual
document datasets and show that our approach outperforms the state-of-the-art
global context-based IE technique. In fact, NIE outperforms existing approaches
in both small and large model sizes. Our on-device implementation of NIE on a
mobile platform that generally requires small models showcases NIE's usefulness
in practical real-world applications.
