SARD: Synthetic Arabic OCR Dataset
- SARD is a large-scale synthetic Arabic OCR dataset composed of 843,622 book-style images rendered in 10 distinct Arabic fonts.
- It provides noise-free, perfectly aligned ground truth with detailed layout metadata, ensuring robust training and evaluation of OCR systems.
- The dataset highlights font-dependent OCR challenges and enables precise benchmarking using metrics like CER, WER, and BLEU.
SARD, short for the Large-Scale Synthetic Arabic OCR Dataset, is a massive, synthetically generated dataset specifically designed to simulate book-style documents for Arabic optical character recognition (OCR) (Nacar et al., 30 May 2025). It was introduced to address the lack of large, diverse, and well-structured datasets that mimic real-world book layouts, a limitation that has hampered training of modern OCR models, especially powerful vision-LLMs. SARD comprises 843,622 document images containing approximately 690 million words, rendered across ten distinct Arabic fonts, and is entirely noise-free and distortion-free, with perfect ground-truth alignment guaranteed by saving the original text used for each image (Nacar et al., 30 May 2025).
1. Definition, scope, and naming
SARD denotes a dataset resource for Arabic OCR rather than a single recognition model. Its stated role is to provide a clean and controlled environment for model training while retaining the typographic and structural properties of book-style Arabic pages. Existing Arabic OCR datasets are described as often focusing on isolated words or lines or being limited in scale, typographic variety, or structural complexity found in books, and SARD is positioned as a response to that specific gap (Nacar et al., 30 May 2025).
The acronym is overloaded in the arXiv literature. Separate works use SARD for “A Human-AI Collaborative Story Generation” (Radwan et al., 2024), “Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance” (Wang et al., 5 Aug 2025), and “A YOLOv8-Based System for Solar Active Region Detection with SDO/HMI Magnetograms” (Pan et al., 29 Jul 2025). In the OCR context, however, SARD refers specifically to the Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition.
2. Corpus design and composition
SARD is organized at the level of full-page document images rather than isolated tokens or single lines. The dataset specification emphasizes scale, typographic diversity, structural regularity, and perfect supervision (Nacar et al., 30 May 2025).
| Property | Specification |
|---|---|
| Full-page document images | 843,622 |
| Total words | Approximately 690 million |
| Page format | A4 (8.27 × 11.69 in), grayscale, 300 DPI |
| Text direction and layout | Right-to-Left, right-justified, line spacing 1.15 |
| Noise model | Entirely noise-free and distortion-free |
| Ground truth | Exact source text stored with layout metadata |
The ten fonts were carefully selected to cover classical, modern, UI-optimized, monospaced and stylized publications. They are: Amiri (classical Naskh), Arial (modern sans-serif), Calibri (digital-era sans-serif), Sakkal Majalla (contemporary publishing), Scheherazade New (manuscript-inspired), Noto Naskh Arabic UI (UI-optimized), Lateef (small-size readability), Thabit (monospaced, technical), Jozoor (stylized forms), and Al-Jazeera-Arabic-Regular (journalistic typography) (Nacar et al., 30 May 2025). The stated rationale is that these ten cover the most prevalent typographic styles in Arabic-language books, ensuring models see a broad spectrum of glyph shapes, ligatures, diacritic placements and whitespace patterns.
The page specification is fixed at A4, grayscale, and 300 DPI, with margins left=0.9 in, right=0.9 in, top=1.0 in, bottom=1.0 in, gutter=0.2 in. Text is Right-to-Left, right-justified, with line spacing 1.15. Words per page are varied per font (e.g., 50–500 words/page) to simulate sparse to dense text blocks, and paragraph structure, line-breaking and hyphenation follow standard book typesetting conventions (Nacar et al., 30 May 2025).
SARD is explicitly free from real-world noise and distortions: it contains no scanning artifacts (skew, blur, ink-bleed). At the same time, anti-aliasing is applied during rendering to mimic printed quality. This combination yields a synthetic corpus that is both typographically rich and procedurally controlled. Future plans include optional noise modules such as Gaussian blur, speckle, JPEG compression, simulated folds to bridge to real scanned documents (Nacar et al., 30 May 2025).
3. Synthetic generation pipeline
The generation process is presented as a four-stage pipeline: text sourcing and preparation, layout and formatting, image rendering, and ground-truth generation (Nacar et al., 30 May 2025).
Text sourcing draws on over 133,000 public-domain Arabic articles across nine domains: Culture, Fatawa, Literature, Bibliography, Publications, Shariah, Social, Translations, News. Preparation includes normalize Unicode, remove unwanted control characters, standardize diacritics, segment into paragraphs. The source selection is randomized to ensure topic and vocabulary diversity (Nacar et al., 30 May 2025).
Layout and formatting are defined via Python scripts (text2image) with LaTeX-like layout parameters. Font size is fixed per font (12 pt for most, 14 pt for Sakkal Majalla and Lateef). The word-wrap and line-breaking algorithms respect Arabic linguistic units and avoid mid-ligature splits, while kerning and letter spacing are controlled by the underlying font engine, namely HarfBuzz through FreeType (Nacar et al., 30 May 2025). This is significant for Arabic OCR because ligature continuity and RTL line formation are major determinants of recognition difficulty.
Image rendering uses high-resolution rasterization (300 DPI) using Pillow/PIL binding to FreeType, with anti-aliasing and subpixel rendering to replicate professional printing appearance. Output is grayscale (Nacar et al., 30 May 2025). Because the images are generated rather than scanned, rendering parameters can be held constant or varied in a fully auditable way.
Ground truth is stored at the page level. For each page image, the exact source text is stored alongside layout metadata (font name, size, word sequence). This ensures perfect supervision for both character- and word-level training (Nacar et al., 30 May 2025). A plausible implication is that SARD is well suited not only to OCR transcription benchmarks but also to supervised pre-training regimes in which exact alignment between rendered page and reference text is essential.
4. Benchmarking protocol and empirical behavior
The benchmarking study evaluates both traditional and deep-learning OCR systems: Tesseract 4, EasyOCR, AIN (Vision-LLM, Heakl et al. 2025), Mistral-OCR, Arabic-large-nougat, Qari-OCR v0.2, and Qari-OCR v0.3 (Nacar et al., 30 May 2025).
The test set contains 2,000 images, defined as 200 randomly selected pages per font. The remaining 841,622 images are described as implicitly available for training and validation, with typical splits: 80 / 10 / 10. All models were evaluated zero-shot on the SARD test set except Qari-OCR which was fine-tuned on a held-out portion of SARD (Nacar et al., 30 May 2025).
Three evaluation metrics are used. Character Error Rate (CER) is defined by
where is substitutions, is deletions, is insertions, and is total characters in ground truth. Word Error Rate (WER) is defined by
where is total words in ground truth. BLEU Score measures n-gram overlap between predicted text block and reference (Nacar et al., 30 May 2025).
The reported averages show distinct behavior across metrics. For WER, Qari v0.2 is best at 0.2194, followed by Mistral-OCR at 0.2248; Tesseract records 0.3492; Qari v0.3 0.4258; Arabic-Nougat 0.4693; EasyOCR 0.7655; and AIN 0.7462 (Nacar et al., 30 May 2025). For CER, Mistral-OCR is best at 0.0510, followed by Qari v0.2 at 0.1578 and Tesseract at 0.1620; the remaining averages are Arabic-Nougat 0.2461, Qari v0.3 0.3932, EasyOCR 0.5956, and AIN 0.7056 (Nacar et al., 30 May 2025). For BLEU, Qari v0.2 is best at 0.7618, followed by Mistral-OCR 0.6700, Tesseract 0.5373, Arabic-Nougat 0.3669, Qari v0.3 0.3132, EasyOCR 0.2908, and AIN 0.1107 (Nacar et al., 30 May 2025).
The paper’s observations emphasize that advanced VLMs (Mistral-OCR, Qari v0.2) vastly outperform both traditional engines and general-purpose OCR libraries. It also reports marked font dependence: for example, Mistral CER on Amiri = 0.0110 vs. on Thabit = 0.0705. In addition, general deep-learning libraries without targeted fine-tuning (EasyOCR, AIN) fare poorly (< 30 % BLEU, > 50 % CER) (Nacar et al., 30 May 2025). This suggests that typographic diversity in SARD is not incidental; it directly exposes model sensitivity to font-dependent glyph morphology and spacing.
5. Technical challenges, limitations, and the synthetic–scanned divide
The analysis section identifies several persistent OCR difficulties. Diacritic recognition and correct placement remain difficult, especially in stylized fonts. Preserving RTL flow and ligature context under varied line breaks stresses standard OCR pipelines. Monospaced and highly condensed text (Thabit) increases insertion/deletion errors (Nacar et al., 30 May 2025). These are not presented as artifacts of the benchmark protocol alone, but as structural challenges revealed by the dataset design.
SARD’s principal advantages are listed as perfect ground truth, controlled variability, clean images, and massive scale without manual annotation cost (Nacar et al., 30 May 2025). Those features make it particularly suitable for pre-training and controlled comparison across architectures. However, the paper also identifies explicit limitations: SARD lacks real-world artifacts (skew, bleed, stains) and uses a simplified single-column layout, no figures, tables or marginalia (Nacar et al., 30 May 2025).
The contrast between synthetic and scanned data is therefore central. SARD is not intended to replicate the full statistics of deteriorated archival material. Rather, it isolates typographic and page-layout variation while excluding scanner and paper defects. This suggests a two-stage research strategy: first learn Arabic typography and page regularities under perfect supervision, then adapt to acquisition noise and heterogeneous document structure.
To bridge the domain gap, the paper proposes three routes: introduce synthetic noise modules (paper texture, random skew, lighting variation); fine-tune VLMs on small subsets of real scanned pages to adapt to scanning artifacts; and employ domain-adversarial training or cycleGAN-style augmentation to simulate scanner characteristics (Nacar et al., 30 May 2025).
6. Research use, recommendations, and projected extensions
The practical recommendations frame SARD as both a training corpus and an evaluation benchmark. The proposed usage pattern is to start with a clean VLM pre-training on the full SARD corpus to learn Arabic typography robustly, reserve at least 5 %–10 % of SARD as a held-out validation/test split stratified by font, fine-tune downstream models on task-specific subsets, and evaluate CER, WER and BLEU jointly to capture character- and word-level performance and overall fluency (Nacar et al., 30 May 2025).
The recommended task-specific subsets include narrow font sets for specialized publications, indicating that the dataset can support both broad pre-training and narrower domain adaptation. This suggests that SARD is useful for experiments on transfer across typographic regimes, especially when the deployment target is a restricted publication style.
Planned extensions are explicitly enumerated: expand font library: add calligraphic, historical and decorative fonts; simulate multi-column layouts, embedded tables, footnotes and bilingual pages; integrate a companion synthetic handwritten dataset; and develop a hybrid corpus: overlay real scanned page backgrounds with synthetic text layers (Nacar et al., 30 May 2025). Each extension targets a concrete limitation of the current release.
The project also provides data and code. The dataset is hosted on Hugging Face, and the generation scripts are available through GitHub via the text2image repository (Nacar et al., 30 May 2025). In research terms, SARD functions as a reproducible substrate for Arabic OCR and document understanding experiments: a large-scale, font-diverse, book-style corpus with exact supervision, strong benchmarking baselines, and a clearly delineated path toward synthetic–real hybridization.
