PICK is a framework that is effective and robust in handling complex documents layout for Key Information Extraction (KIE) by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the ...

July 18, 2020 - Abstract page for arXiv paper 2004.07464: PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

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 ...

Recognition (NER) [2] framework, processing the plain text · as a linear sequence result in ignoring most of valuable visual · and non-sequential information (e.g., text, position, layout, and · image) of documents for KIE. The main challenge faced by · many researchers is how to fully and efficiently exploit both · textual and visual features of documents to get a richer semantic · representation that is crucial for extracting key ...

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July 5, 2022 - For document-related tasks as part of layout analysis and information extraction, we implemented PICK (Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks)

April 16, 2020 - PICK is introduced, 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 ...

March 17, 2024 - In this paper, we propose PICK, a robust and effective method shown in Figure 2(d), Processing Key Information Extraction from Documents using improved Graph Learning-Convolutional NetworKs, to improve extraction ability by automatically making ...

December 7, 2020 - PICK is a framework that is effective and robust in handling complex documents layout for Key Information Extraction (KIE) by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the ...

April 16, 2020 - Information extraction from receipts is the process to recognize text and extract key texts from scanned receipts. This task plays a critical role in a wide range of applications in finance, accounting and taxation.

January 25, 2026 - Wenwen Yu, Ning Lu, Xianbiao Qi, Ping Gong, Rong Xiao: PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks.

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April 16, 2020 - 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 https://github.com/wenwenyu/PICK-pytorch. PDF Abstract ... Graph Learning Key Information Extraction Optical Character Recognition Optical Character Recognition (OCR) Text Detection

November 24, 2025 - Key Information Extraction (KIE) is the process of automatically identifying, localizing, and structuring key semantic content from unstructured documents.

May 13, 2026 - Để hiểu rõ phương pháp 1d) này hơn thì trong bài viết này mình sẽ giải thích một phương pháp đạt kết quả cao trên tập SROI2019, có tên gọi là "Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (PICK).

A curated list of papers about key information extraction. Paperswithcode links will be preferred. Welcome contributions! ... PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

May 17, 2022 - nlp pdf machine-learning natural-language-processing awesome ocr deep-learning information-extraction awesome-list pdf-documents document-analysis rpa unstructured-data robotic-process-automation document-layout-analysis document-understanding key-information-extraction document-ai document-intelligence intelligent-processing ... Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)