Financial Table Extraction in Image Documents

Abstract

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.

References in corpus (4)

• scikit-image: Image processing in Python
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• Cascade R-CNN: Delving into High Quality Object Detection
• Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition

Cited by in corpus (1)

• FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning