Financial Table Extraction in Image Documents
This addresses the challenge of processing financial documents locked in image formats, though it appears incremental by combining existing deep learning techniques.
The paper tackled the problem of extracting tabular content from image documents in financial services, presenting an end-to-end pipeline that identifies, extracts, and transcribes tables while preserving spatial relations with high fidelity.
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.