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

arXiv: https://arxiv.org/abs/2004.07464 · HTML: https://mlsanity.com/paper/2004.07464/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 30 of 72)

- [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://mlsanity.com/paper/2202.13669/) (2022)
- [StrucTexT: Structured Text Understanding with Multi-Modal Transformers](https://mlsanity.com/paper/2108.02923/) (2021)
- [OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition](https://mlsanity.com/paper/2403.19128/) (2024)
- [Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution](https://mlsanity.com/paper/2102.06732/) (2021)
- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)
- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution](https://mlsanity.com/paper/2305.07498/) (2023)
- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations](https://mlsanity.com/paper/2105.06220/) (2021)
- [MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding](https://mlsanity.com/paper/2108.06543/) (2021)
- [GeoLayoutLM: Geometric Pre-training for Visual Information Extraction](https://mlsanity.com/paper/2304.10759/) (2023)
- [HRVDA: High-Resolution Visual Document Assistant](https://mlsanity.com/paper/2404.06918/) (2024)
- [ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents](https://mlsanity.com/paper/2105.11672/) (2021)
- [Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering](https://mlsanity.com/paper/2306.00526/) (2023)
- [Entity Relation Extraction as Dependency Parsing in Visually Rich Documents](https://mlsanity.com/paper/2110.09915/) (2021)
- [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025)
- [ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction](https://mlsanity.com/paper/2106.10786/) (2021)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images](https://mlsanity.com/paper/2306.03287/) (2023)
- [MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction](https://mlsanity.com/paper/2106.12940/) (2021)
- [Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding](https://mlsanity.com/paper/2206.13155/) (2022)
- [DocTr: Document Transformer for Structured Information Extraction in Documents](https://mlsanity.com/paper/2307.07929/) (2023)
- [Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration](https://mlsanity.com/paper/2309.01131/) (2023)
- [Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks](https://mlsanity.com/paper/2208.11168/) (2022)
- [LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding](https://mlsanity.com/paper/2104.08405/) (2021)
- [Cost-effective End-to-end Information Extraction for Semi-structured Document Images](https://mlsanity.com/paper/2104.08041/) (2021)
- [Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models](https://mlsanity.com/paper/2506.20168/) (2025)
- [Business Document Information Extraction: Towards Practical Benchmarks](https://mlsanity.com/paper/2206.11229/) (2022)
- [FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding](https://mlsanity.com/paper/2505.17330/) (2025)
- [ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding](https://mlsanity.com/paper/2209.08569/) (2022)

## Abstract (arXiv)

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 https://github.com/wenwenyu/PICK-pytorch.