# SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition

arXiv: https://arxiv.org/abs/2505.24600 · HTML: https://mlsanity.com/paper/2505.24600/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition](https://mlsanity.com/paper/1706.06720/) (2017)
- [AIN: The Arabic INclusive Large Multimodal Model](https://mlsanity.com/paper/2502.00094/) (2025)
- [Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction](https://mlsanity.com/paper/2411.17835/) (2024)
- [AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition](https://mlsanity.com/paper/1411.4670/) (2014)

## Built on by (most cited first, 6 of 6)

- [Cross-Lingual SynthDocs: A Large-Scale Synthetic Corpus for Any to Arabic OCR and Document Understanding](https://mlsanity.com/paper/2511.04699/) (2025)
- [A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation](https://mlsanity.com/paper/2509.02864/) (2025)
- [MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset](https://mlsanity.com/paper/2601.21512/) (2026)
- [When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study](https://mlsanity.com/paper/2608.22366/) (2026)
- [AraMS-28k: The Largest Publicly Released Line-Level Dataset of Historical Arabic Manuscripts with Margin and Insertion-Anchor Annotations](https://mlsanity.com/paper/2608.26921/) (2026)
- [DDVR-Net: Degradation-Aware Decoupling and Visibility-Guided Restoration for Historical Document Recognition](https://mlsanity.com/paper/2610.16956/) (2026)

## Abstract (arXiv)

Arabic Optical Character Recognition (OCR) is essential for converting vast amounts of Arabic print media into digital formats. However, training modern OCR models, especially powerful vision-language models, is hampered by the lack of large, diverse, and well-structured datasets that mimic real-world book layouts. Existing Arabic OCR datasets often focus on isolated words or lines or are limited in scale, typographic variety, or structural complexity found in books. To address this significant gap, we introduce SARD (Large-Scale Synthetic Arabic OCR Dataset). SARD is a massive, synthetically generated dataset specifically designed to simulate book-style documents. It comprises 843,622 document images containing 690 million words, rendered across ten distinct Arabic fonts to ensure broad typographic coverage. Unlike datasets derived from scanned documents, SARD is free from real-world noise and distortions, offering a clean and controlled environment for model training. Its synthetic nature provides unparalleled scalability and allows for precise control over layout and content variation. We detail the dataset's composition and generation process and provide benchmark results for several OCR models, including traditional and deep learning approaches, highlighting the challenges and opportunities presented by this dataset. SARD serves as a valuable resource for developing and evaluating robust OCR and vision-language models capable of processing diverse Arabic book-style texts.