  Paper documents are widely used as an irreplaceable channel of information in
many fields, especially in financial industry, fostering a great amount of
demand for systems which can convert document images into structured data
representations. In this paper, we present a machine learning framework for
data ingestion in document images, which processes the images uploaded by users
and return fine-grained data in JSON format. Details of model architectures,
design strategies, distinctions with existing solutions and lessons learned
during development are elaborated. We conduct abundant experiments on both
synthetic and real-world data in State Street. The experimental results
indicate the effectiveness and efficiency of our methods.
