2020

Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context

Zheng, Xinyi, Burdick, Doug, Popa, Lucian et al.

Understand

Documents are often used for knowledge sharing and preservation in business and science, within which are tables that capture most of the critical data.

Unfortunately, most documents are stored and distributed as PDF or scanned images, which fail to preserve logical table structure.
Recent vision-based deep learning approaches have been proposed to address this gap, but most still cannot achieve state-of-the-art results.
We present Global Table Extractor (GTE), a vision-guided systematic framework for joint table detection and cell structured recognition, which could be built on top of any object detection model.

Built on

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