  Table extraction has long been a pervasive problem in financial services.
This is more challenging in the image domain, where content is locked behind
cumbersome pixel format. Luckily, advances in deep learning for image
segmentation, OCR, and sequence modeling provides the necessary heavy lifting
to achieve impressive results. This paper presents an end-to-end pipeline for
identifying, extracting and transcribing tabular content in image documents,
while retaining the original spatial relations with high fidelity.
