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https://github.com/entropy2333/awesome-key-information-extraction
A curated list of papers about key information extraction.
https://github.com/entropy2333/awesome-key-information-extraction
List: awesome-key-information-extraction
kie ocr
Last synced: 3 months ago
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A curated list of papers about key information extraction.
- Host: GitHub
- URL: https://github.com/entropy2333/awesome-key-information-extraction
- Owner: entropy2333
- Created: 2022-06-25T10:27:59.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-05T07:54:45.000Z (4 months ago)
- Last Synced: 2024-08-08T16:13:01.666Z (3 months ago)
- Topics: kie, ocr
- Homepage:
- Size: 23.4 KB
- Stars: 66
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Awesome Key Infomation Extraction
[](https://awesome.re)
A curated list of papers about key information extraction.
Paperswithcode links will be preferred.
Welcome contributions!
## Tabel of Contents
- [Awesome Key Infomation Extraction](#awesome-key-infomation-extraction)
- [Tabel of Contents](#tabel-of-contents)
- [Datasets](#datasets)
- [Survey](#survey)
- [Toolkits](#toolkits)
- [Models](#models)
- [:star:LLM-Based](#starllm-based)
- [Graph-Based](#graph-based)
- [Transformer-Based](#transformer-based)
- [Grid-Based](#grid-based)
- [End-to-end](#end-to-end)
- [Others](#others)
- [Related Repositories](#related-repositories)
## Datasets
| Name | Title | Links |
| :----------: | --------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------: |
| DUE | DUE: End-to-End Document Understanding Benchmark | [[link]](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/069059b7ef840f0c74a814ec9237b6ec-Abstract-round2.html) |
| RVL-CDIP | Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval | [[link]](https://paperswithcode.com/dataset/rvl-cdip)[[download]](https://www.cs.cmu.edu/~aharley/rvl-cdip/) |
| SROIE | ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction | [[link]](https://paperswithcode.com/dataset/sroie)[[download]](https://rrc.cvc.uab.es/?ch=13&com=downloads) |
| FUNSD | FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents | [[link]](https://paperswithcode.com/dataset/funsd)[[download]](https://guillaumejaume.github.io/FUNSD/) |
| XFUND | XFUND: A Multilingual Form Understanding Benchmark | [[link]](https://github.com/doc-analysis/XFUND) |
| CORD | CORD: A Consolidated Receipt Dataset for Post-OCR Parsing | [[link]](https://github.com/clovaai/cord) |
| EPHOIE | Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution | [[link]](https://github.com/HCIILAB/EPHOIE) |
| EATEN | EATEN: Entity-aware Attention for Single Shot Visual Text Extraction | [[link]](https://github.com/beacandler/EATEN) |
| Train Ticket | PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks | [[link]](https://github.com/wenwenyu/PICK-pytorch)[[download]](https://drive.google.com/file/d/1o8JktPD7bS74tfjz-8dVcZq_uFS6YEGh/view) |
| POIE | Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution | [[link]](https://github.com/jfkuang/CFAM)[[download]](https://drive.google.com/file/d/1eEMNiVeLlD-b08XW_GfAGfPmmII-GDYs/view?usp=share_link) |
## Survey
| Year | Title | Links |
| ---- | ------------------------------------------------------- | :------------------------------------------------------------------------------: |
| 2023 | On the Hidden Mystery of OCR in Large Multimodal Models | [[link]](https://paperswithcode.com/paper/on-the-hidden-mystery-of-ocr-in-large) |
| 2021 | Document AI: Benchmarks, Models and Applications | [[link]](https://paperswithcode.com/paper/document-ai-benchmarks-models-and) |
## Toolkits
| Year | Title | Links |
| ---- | ------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------: |
| 2022 | DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding | [[paper]](https://arxiv.org/pdf/2207.06695v1.pdf)[[code]](https://github.com/hikopensource/davar-lab-ocr) |
| 2021 | MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding | [[paper]](https://arxiv.org/pdf/2108.06543v1.pdf)[[code]](https://github.com/open-mmlab/mmocr) |
| 2020 | PP-OCR: A Practical Ultra Lightweight OCR System | [[paper]](https://arxiv.org/pdf/2009.09941v3.pdf)[[code]](https://github.com/PaddlePaddle/PaddleOCR) |
| 2024 | ANLS* -- A Universal Document Processing Metric for Generative Large Language Models | [[paper]](https://arxiv.org/pdf/2402.03848)[[code]](https://github.com/deepopinion/anls_star_metric) |
## Models
### :star:LLM-Based
| Pub. | Year | Title | Links |
| :---: | :---: | --------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------: |
| ICML | 2023 | BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models | [[link]](https://paperswithcode.com/paper/blip-2-bootstrapping-language-image-pre) |
| Arxiv | 2023 | InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning | [[link]](https://paperswithcode.com/paper/instructblip-towards-general-purpose-vision) |
| Arxiv | 2023 | MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models | [[link]](https://paperswithcode.com/paper/minigpt-4-enhancing-vision-language) |
| Arxiv | 2023 | Visual Instruction Tuning | [[link]](https://paperswithcode.com/paper/visual-instruction-tuning) |
| Arxiv | 2023 | Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond | [[link]](https://paperswithcode.com/paper/qwen-vl-a-frontier-large-vision-language) |
| Arxiv | 2023 | mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality | [[link]](https://paperswithcode.com/paper/mplug-owl-modularization-empowers-large) |
| Arxiv | 2023 | mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding | [[link]](https://paperswithcode.com/paper/mplug-docowl-modularized-multimodal-large) |
| Arxiv | 2023 | mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration | [[link]](https://paperswithcode.com/paper/mplug-owl2-revolutionizing-multi-modal-large) |
| Arxiv | 2023 | Otter: A Multi-Modal Model with In-Context Instruction Tuning | [[link]](https://paperswithcode.com/paper/otter-a-multi-modal-model-with-in-context) |
| Arxiv | 2023 | UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model | [[link]](https://paperswithcode.com/paper/ureader-universal-ocr-free-visually-situated) |
| Blog | 2023 | Fuyu-8B: A Multimodal Architecture for AI Agents | [[blog]](https://www.adept.ai/blog/fuyu-8b)[[model]](https://huggingface.co/adept/fuyu-8b) |
### Graph-Based
| Pub. | Year | Title | Links |
| :----------: | :---: | --------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------: |
| ICDAR | 2023 | LayoutGCN: A Lightweight Architecture for Visually Rich Document Understanding | [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-41682-8_10) |
| ACL-Findings | 2021 | Spatial Dependency Parsing for Semi-Structured Document Information Extraction | [[link]](https://paperswithcode.com/paper/spatial-dependency-parsing-for-2d-document) |
| Arxiv | 2021 | Spatial Dual-Modality Graph Reasoning for Key Information Extraction | [[link]](https://paperswithcode.com/paper/spatial-dual-modality-graph-reasoning-for-key) |
| ICPR | 2020 | PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks | [[link]](https://paperswithcode.com/paper/pick-processing-key-information-extraction) |
### Transformer-Based
| Pub. | Year | Title | Links |
| :----: | :---: | ------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------: |
| ACL | 2022 | LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding | [[link]](https://paperswithcode.com/paper/lilt-a-simple-yet-effective-language) |
| ACL | 2022 | FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction | [[link]](https://paperswithcode.com/paper/formnet-structural-encoding-beyond-sequential) |
| CVPR | 2022 | XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding | [[link]](https://paperswithcode.com/paper/xylayoutlm-towards-layout-aware-multimodal) |
| Arxiv | 2022 | LoPE: Learnable Sinusoidal Positional Encoding for Improving Document Transformer Model | [[link]](https://paperswithcode.com/paper/lope-learnable-sinusoidal-positional-encoding) |
| Arxiv | 2022 | LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking | [[link]](https://paperswithcode.com/paper/layoutlmv3-pre-training-for-document-ai-with) |
| Arxiv | 2022 | ERNIE-Layout: Layout-Knowledge Enhanced Multi-modal Pre-training for Document Understanding | [[link]](https://paperswithcode.com/paper/ernie-layout-layout-knowledge-enhanced-multi) |
| AAAI | 2022 | BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents | [[link]](https://paperswithcode.com/paper/bros-a-layout-aware-pre-trained-language) |
| ICDAR | 2021 | ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents | [[link]](https://paperswithcode.com/paper/vibertgrid-a-jointly-trained-multi-modal-2d)[[code]](https://github.com/ZeningLin/ViBERTgrid-PyTorch) |
| Arxiv | 2021 | TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models | [[link]](https://paperswithcode.com/paper/trocr-transformer-based-optical-character) |
| ACM-MM | 2021 | StrucTexT: Structured Text Understanding with Multi-Modal Transformers | [[link]](https://paperswithcode.com/paper/structext-structured-text-understanding-with) |
| ACL | 2021 | LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding | [[link]](https://paperswithcode.com/paper/layoutlmv2-multi-modal-pre-training-for) |
| KDD | 2020 | LayoutLM: Pre-training of Text and Layout for Document Image Understanding | [[link]](https://paperswithcode.com/paper/layoutlm-pre-training-of-text-and-layout-for) |
### Grid-Based
| Pub. | Year | Title | Links |
| :---: | :---: | ------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------: |
| ICDAR | 2021 | ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents | [[link]](https://paperswithcode.com/paper/vibertgrid-a-jointly-trained-multi-modal-2d) |
| ICDAR | 2021 | VisualWordGrid: Information Extraction From Scanned Documents Using A Multimodal Approach | [[link]](https://paperswithcode.com/paper/visualwordgrid-information-extraction-from-1) |
| NIPS | 2019 | BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding | [[link]](https://paperswithcode.com/paper/bertgrid-contextualized-embedding-for-2d) |
| EMNLP | 2018 | Chargrid: Towards Understanding 2D Documents | [[link]](https://paperswithcode.com/paper/chargrid-towards-understanding-2d-documents) |
### End-to-end
| Pub. | Year | Title | Links |
| :----: | :---: | ------------------------------------------------------------------------------------ | :--------------------------------------------------------------------------------------: |
| ICDAR | 2023 | Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution | [[link]](https://paperswithcode.com/paper/visual-information-extraction-in-the-wild) |
| ICML | 2023 | Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding | [[link]](https://paperswithcode.com/paper/pix2struct-screenshot-parsing-as-pretraining) |
| ECCV | 2022 | OCR-free Document Understanding Transformer | [[link]](https://paperswithcode.com/paper/donut-document-understanding-transformer) |
| Arxiv | 2022 | TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents | [[link]](https://paperswithcode.com/paper/trie-towards-end-to-end-information) |
| ICCV | 2021 | DocFormer: End-to-End Transformer for Document Understanding | [[link]](https://paperswithcode.com/paper/docformer-end-to-end-transformer-for-document) |
| ACM-MM | 2020 | TRIE: End-to-End Text Reading and Information Extraction for Document Understanding | [[link]](https://paperswithcode.com/paper/trie-end-to-end-text-reading-and-information) |
| ICDAR | 2019 | EATEN: Entity-aware Attention for Single Shot Visual Text Extraction | [[link]](https://paperswithcode.com/paper/eaten-entity-aware-attention-for-single-shot) |
### Others
| Pub. | Year | Title | Links |
| :---: | :---: | ------------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------: |
| ICDAR | 2023 | Information Extraction from Documents: Question Answering vs Token Classification in real-world setups | [[link]](https://paperswithcode.com/paper/information-extraction-from-documents) |
## Related Repositories
- https://paperswithcode.com/task/key-information-extraction
- https://github.com/tstanislawek/awesome-document-understanding/blob/main/topics/kie/README.md
- :star:https://github.com/SCUT-DLVCLab/Document-AI-Recommendations#vie
## Star History
[](https://star-history.com/#entropy2333/awesome-key-information-extraction&Date)