---
title: "ICDAR (A): papers and universities"
description: "ICDAR (IEEE International Conference on Document Analysis and Recognition) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 452 of its papers from 2024–2026 (154 in 2024, 149 in 2025, 149 in 2026). By the countries of the authors' institutions, the most papers come from China (126), France (60) and India (50)."
url: https://scholar4ai.com/proceedings/icdar
markdown_url: https://scholar4ai.com/proceedings/icdar.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ICDAR (IEEE International Conference on Document Analysis and Recognition) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 452 of its papers from 2024–2026 (154 in 2024, 149 in 2025, 149 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# ICDAR: IEEE International Conference on Document Analysis and Recognition — papers and institutions, 2024–2026

Papers of ICDAR, IEEE International Conference on Document Analysis and Recognition (CORE rank A; field: Data management and data science), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- ICDAR (IEEE International Conference on Document Analysis and Recognition) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 452 of its papers from 2024–2026 (154 in 2024, 149 in 2025, 149 in 2026).
- By the countries of the authors' institutions, the most papers come from China (126), France (60) and India (50).
- The institutions with the most papers are Computer Vision Center (27), Universitat Autònoma de Barcelona (26) and Centre National de la Recherche Scientifique (25).
- Its most-cited paper of these years is “A Hybrid Approach for Document Layout Analysis in Document Images” (2024), with 21 citations.

## Papers per year

*ICDAR papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 154 | 149 |
| 2025 | 149 | 142 |
| 2026 | 149 | 145 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S106296714](https://openalex.org/S106296714) | Papers of the volumes titled “Document Analysis and Recognition – ICDAR <year>” (LNCS), found by DOI through Crossref |

## Who publishes there

*Institutions with the most ICDAR papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | Computer Vision Center | 27 |
| 2 | Universitat Autònoma de Barcelona | 26 |
| 3 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 25 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 24 |
| 5 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 21 |
| 6 | Institute of Automation | 19 |
| 7 | International Institute of Information Technology, Hyderabad | 19 |
| 8 | Kyushu University | 14 |
| 9 | Laboratoire Informatique, Image et Interaction (L3i) | 13 |
| 10 | La Rochelle Université | 13 |

*Countries with the most ICDAR papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 126 |
| 2 | France | 60 |
| 3 | India | 50 |
| 4 | Germany | 48 |
| 5 | Spain | 44 |
| 6 | Japan | 34 |
| 7 | United States | 32 |
| 8 | Italy | 19 |
| 9 | Switzerland | 15 |
| 10 | United Kingdom | 14 |

*Keywords with the most ICDAR papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | handwriting recognition | 36 |
| 2 | optical character recognition | 34 |
| 3 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 32 |
| 4 | document understanding | 22 |
| 5 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 21 |
| 6 | document layout analysis | 16 |
| 7 | benchmark dataset | 14 |
| 8 | character error rate | 14 |
| 9 | document image analysis | 13 |
| 10 | diffusion models | 12 |

## Most-cited papers

*Most-cited ICDAR papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 21 | [A Hybrid Approach for Document Layout Analysis in Document Images](https://doi.org/10.1007/978-3-031-70546-5_2) | 2024-01-01 | Tahira Shehzadi, Didier Stricker, Muhammad Zeshan Afzal |
| 2 | 18 | [Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription](https://doi.org/10.1007/978-3-031-70552-6_2) | 2024-01-01 | Antonio Ríos-Vila, Jorge Calvo-Zaragoza, Thierry Paquet |
| 3 | 18 | [ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition](https://doi.org/10.1007/978-3-031-70549-6_2) | 2024-01-01 | Jianhua Zhu, Liangcai Gao, Wenqi Zhao |
| 4 | 17 | [DLAFormer: An End-to-End Transformer For Document Layout Analysis](https://doi.org/10.1007/978-3-031-70546-5_3) | 2024-01-01 | Jiawei Wang, Kai Hu, Qiang Huo |
| 5 | 15 | [CATMuS Medieval: A Multilingual Large-Scale Cross-Century Dataset in Latin Script for Handwritten Text Recognition and Beyond](https://doi.org/10.1007/978-3-031-70543-4_11) | 2024-01-01 | Thibault Clérice, Ariane Pinche, Malamatenia Vlachou-Efstathiou et al. |
| 6 | 14 | [Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library](https://doi.org/10.1007/978-3-031-70549-6_23) | 2024-01-01 | Solène Tarride, Yoann Schneider, Marie Generali-Lince et al. |
| 7 | 11 | [Practical End-to-End Optical Music Recognition for Pianoform Music](https://doi.org/10.1007/978-3-031-70552-6_4) | 2024-01-01 | Jiří Mayer, Milan Straka, Jan Hajič et al. |
| 8 | 11 | [Towards End-to-End Semi-supervised Table Detection with Semantic Aligned Matching Transformer](https://doi.org/10.1007/978-3-031-70549-6_18) | 2024-01-01 | Tahira Shehzadi, Shalini Sarode, Didier Stricker et al. |
| 9 | 11 | [Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction](https://doi.org/10.1007/978-3-031-70533-5_11) | 2024-01-01 | Pengjie Wang, Kaile Zhang, Xinyu Wang et al. |
| 10 | 11 | [Self-supervised Vision Transformers for Writer Retrieval](https://doi.org/10.1007/978-3-031-70536-6_23) | 2024-01-01 | Tim Raven, Arthur Matei, Gernot A. Fink |

## Cite this page

Scholar4AI. (2026). ICDAR: IEEE International Conference on Document Analysis and Recognition — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/icdar

```bibtex
@misc{scholar4ai-proceedings-icdar,
  author = {{Scholar4AI}},
  title = {ICDAR: IEEE International Conference on Document Analysis and Recognition — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/icdar}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).