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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of resources for Document Understanding (DU) topic
| Date | Stars |
|---|---|
| 2026-07-24 | 1525 |
| 2026-07-25 | 1525 |
| 2026-07-28 | 1525 |
| 2026-07-30 | 1525 |
| 2026-08-06 | 1525 |
Today
— stars today
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# Awesome Document Understanding [](https://awesome.re)
A curated list of resources for Document Understanding (DU) topic related to Intelligent Document Processing (IDP), which is relative to Robotic Process Automation (RPA) from unstructured data, especially form Visually Rich Documents (VRDs).
**Note 1: bolded positions are more important then others.**
**Note 2: due to the novelty of the field, this list is under construction - contributions are welcome (thank you in advance!).** Please remember to use following convention:
* [Title of a publication / dataset / resource title](https://arxiv.org), \[[code/data/Website](https://github.com/example/test) \]
<details>
<summary> List of authors <em>Conference/Journal name</em> Year </summary>
Dataset size: Train(no of examples), Dev(no of examples), Test(no of examples) [Optional for dataset papers/resources]; Abstract/short description ...
</details>
<br/><br/>
<p align="center">
<a href="https://openreview.net/forum?id=rNs2FvJGDK">
<img src="images/du_example.png">
</a>
</p>
<br/><br/>
# Table of contents
1. [Introduction](#introduction)
1. [Research topics](#research-topics)
1. [Key Information Extraction (KIE)](topics/kie/README.md)
1. [Document Layout Analysis (DLA)](topics/dla/README.md)
1. [Document Question Answering (DQA)](topics/dqa/README.md)
1. [Scientific Document Understanding (SDU)](topics/sdu/README.md)
1. [Optical Character Recognition (OCR)](topics/ocr/README.md)
1. [Related](topics/related/README.md)
1. [General](topics/related/README.md#general)
1. [Tabular Data Comprehension (TDC)](topics/related/README.md#tabular-data-comprehension)
1. [Robotic Process Automation (RPA)](topics/related/README.md#robotic-process-automation)
1. [Others](#others)
1. [Resources](#resources)
1. [Datasets for Pre-training Language Models](#datasets-for-pre-training-language-models)
1. [PDF processing tools](#pdf-processing-tools)
1. [Conferences / workshops](#conferences-workshops)
1. [Blogs](#blogs)
1. [Solutions](#solutions)
1. [Examples](#examples)
1. [Visually Rich Documents (VRDs)](#visually-rich-documents)
1. [Key Information Extraction (KIE)](#key-information-extraction)
1. [Document Layout Analysis (DLA)](#document-layout-analysis)
1. [Document Question Answering (DQA)](#document-question-answering)
1. [Inspirations](#inspirations)
# Introduction
Documents are a core part of many businesses in many fields such as law, finance, and technology among others. Automatic understanding of documents such as invoices, contracts, and resumes is lucrative, opening up many new avenues of business. The fields of natural language processing and computer vision have seen tremendous progress through the development of deep learning such that these methods have started to become infused in contemporary document understanding systems. [source](https://arxiv.org/abs/2011.13534)
### Papers
#### 2023
* [DocILE Benchmark for Document Information Localization and Extraction](https://arxiv.org/abs/2302.05658), \[[Website](https://docile.rossum.ai)\] \[[benchmark](https://rrc.cvc.uab.es/?ch=26)\] \[[code](https://github.com/rossumai/docile) \]
<details>
<summary> Štěpán Šimsa, Milan Šulc, Michal Uřičář, Yash Patel, Ahmed Hamdi, Matěj Kocián, Matyáš Skalický, Jiří Matas, Antoine Doucet, Mickaël Coustaty, Dimosthenis Karatzas <em>arxiv pre-print</em> 2023 </summary>
This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly~1M unlabeled documentsExcerpt of 35,354 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bf54e21ac26ec67b, topic:nlp, topic:natural-language-processing, topic:information-extraction
matched fp:bf54e21ac26ec67b, topic:document-understanding, name:document understanding, desc:document understanding
matched fp:bf54e21ac26ec67b, topic:awesome, topic:awesome-list, desc:curated list
