Computer vision with state-of-the-art deep learning models has achieved huge
success in the field of Optical Character Recognition (OCR) including text
detection and recognition tasks recently. However, Key Information Extraction
(KIE) from documents as the downstream task of OCR, having a large number of
use scenarios in real-world, remains a challenge because documents not only
have textual features extracting from OCR systems but also have semantic visual
features that are not fully exploited and play a critical role in KIE. Too
little work has been devoted to efficiently make full use of both textual and
visual features of the documents. In this paper, we introduce PICK, a framework
that is effective and robust in handling complex documents layout for KIE by
combining graph learning with graph convolution operation, yielding a richer
semantic representation containing the textual and visual features and global
layout without ambiguity. Extensive experiments on real-world datasets have
been conducted to show that our method outperforms baselines methods by
significant margins. Our code is available at
this https URL

该文介绍了使用图形学习与图形卷积操作相结合的 PICK 框架，以处理 KIE 中复杂文档布局，从而获得包含文本和视觉特征和全局布局的更丰富的语义表示，并通过实验表明了该方法的性能优于基线方法。