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Computer vision with state-of-the-art deep learning models has achieved hugesuccess in the field of Optical Character Recognition (OCR) including textdetection and recognition tasks recently. However, Key Information Extraction(KIE) from documents as the downstream task of OCR, having a large number ofuse scenarios in real-world, remains a challenge because documents not onlyhave textual features extracting from OCR systems but also have semantic visualfeatures that are not fully exploited and play a critical role in KIE. Toolittle work has been devoted to efficiently make full use of both textual andvisual features of the documents. In this paper, we introduce PICK, a frameworkthat is effective and robust in handling complex documents layout for KIE bycombining graph learning with graph convolution operation, yielding a richersemantic representation containing the textual and visual features and globallayout without ambiguity. Extensive experiments on real-world datasets havebeen conducted to show that our method outperforms baselines methods bysignificant margins. Our code is available athttps://github.com/wenwenyu/PICK-pytorch.

PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks