README.md

---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-to-text
- image-classification
- image-feature-extraction
task_ids: []
pretty_name: "SAQR: A Paired Printed–Handwritten Arabic Line Dataset"
dataset_info:
  features:
    - name: pair_id
      dtype: int64
    - name: gt_image
      dtype: image
    - name: hw_image
      dtype: image
    - name: text
      dtype: string
    - name: category
      dtype:
        class_label:
          names:
            '0': boys
            '1': girls
    - name: split
      dtype: string
    - name: student_id
      dtype: string
    - name: form_creator
      dtype: string
  splits:
    - name: train
      num_examples: 1575
    - name: validation
      num_examples: 335
    - name: test
      num_examples: 353
---

# SAQR: A Paired Printed–Handwritten Arabic Line Dataset for Handwriting Recognition and Cross-Modal Retrieval

**Paper**: *SAQR: A Paired Printed–Handwritten Arabic Line Dataset for Handwriting Recognition and Cross-Modal Retrieval*  
**Journal**: Scientific Data (Nature/Springer) — under review  
**Contact**: mahmoud.salah@aun.edu.eg

---

## Dataset Summary

SAQR is a large-scale paired **Printed Ground Truth – Handwritten (GT-HW)** Arabic dataset collected from **331 Arabic-speaking students** aged 12–22. Each sample pairs a **printed reference line image** with the **corresponding student handwriting**, providing rich supervision for Arabic Handwritten Text Recognition (HTR) and related vision tasks.

| Property | Value |
|----------|-------|
| **Total pairs** | 2,263 GT-HW line pairs |
| **Writers** | 331 students (age 12–22) |
| **Gender** | 1,207 male / 1,056 female |
| **Total words** | 25,352 |
| **Total characters** | 111,437 Arabic characters |
| **Unique vocabulary** | 4,047 word types |
| **Unique characters** | 47 |
| **Avg. line length** | 49.2 chars / 11.2 words |
| **Splits** | Train 1,575 / Val 335 / Test 353 (writer-independent) |
| **Language** | Arabic (Modern Standard Arabic / formal prose) |
| **License** | CC-BY 4.0 |

## Dataset Preview

Sample GT–HW line pairs from SAQR, showing the range of handwriting styles across the writer population (printed ground truth on top, student handwriting below):

![Sample GT-HW pairs](sample_pairs.png)

## Supported Tasks

| Task | Description |
|------|-------------|
| **Handwritten Text Recognition (HTR)** | Transcribe `hw_image` → Arabic text |
| **Gender Classification** | Predict writer gender from `hw_image` |
| **GT-HW Cross-Modal Matching** | Match `gt_image` ↔ `hw_image` across 353 candidates |

## Dataset Structure

Each sample contains:
- `pair_id` — unique integer identifier
- `gt_image` — image of the **printed** Arabic text line (Ground Truth)
- `hw_image` — image of the **handwritten** copy (student writing)
- `text` — Arabic transcription string
- `category` — writer gender (`0=boys` / `1=girls`)
- `split` — `train` / `val` / `test`
- `student_id` — anonymized form identifier
- `form_creator` — form template identifier

## Loading the Dataset

### Full dataset
```python
from datasets import load_dataset

ds = load_dataset("mahmoudsalah01/SAQR")
train = ds["train"]
val   = ds["validation"]
test  = ds["test"]

# Access a sample
sample = train[0]
print(sample["text"])        # Arabic transcription
sample["gt_image"].show()    # Printed GT line
sample["hw_image"].show()    # Handwritten line
```

### HTR task (HW image → text)
```python
from datasets import load_dataset
ds = load_dataset("mahmoudsalah01/SAQR", split="train")
for sample in ds:
    image = sample["hw_image"]   # Input image
    label = sample["text"]       # Target transcription
```

### Gender classification task
```python
from datasets import load_dataset
ds = load_dataset("mahmoudsalah01/SAQR", split="train")
for sample in ds:
    image  = sample["hw_image"]    # Input: handwriting image
    gender = sample["category"]    # 0=boys, 1=girls
```

## Benchmark Results

### Task 1 — Handwritten Text Recognition

| Method | CER ↓ | WER ↓ |
|--------|--------|--------|
| Tesseract 4.0 (zero-shot) | 0.565 | 1.061 |
| EasyOCR (zero-shot) | 0.485 | 1.045 |
| TrOCR-Base (fine-tuned, **ours**) | **0.533** | **0.721** |

### Task 2 — Gender Classification from Handwriting

| Method | Accuracy | F1-Macro |
|--------|----------|----------|
| ViT-Base (fine-tuned, **ours**) | **73.5%** | 0.71 |

### Task 3 — GT-HW Cross-Modal Matching (pool = 353)

| Method | R@1 | R@10 | MRR |
|--------|-----|------|-----|
| CLIP ViT-B/32 (zero-shot) | 0.6% | 7.1% | 3.2% |
| DINOv2 Siamese | 1.7% | 11.0% | 5.6% |
| Fine-tuned CLIP | 5.9% | 26.3% | 12.9% |
| Siamese ViT (InfoNCE) | 5.4% | 36.5% | 15.0% |
| **Siamese ViT + Hard Neg (ours)** | **10.8%** | **31.7%** | **17.7%** |

## Collection Methodology

Students copied text lines from printed forms (Arabic formal prose, sourced from newspaper articles) onto A4 paper sheets. Forms were scanned at 300 DPI, line-segmented, and paired by form index. Transcriptions were extracted from original `.docx` form templates. Splits are **writer-independent**: no writer appears in more than one split.

## License

[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)

## Contact

**Mahmoud Salah** — mahmoud.salah@aun.edu.eg  
For questions or issues, please open a [Discussion](https://huggingface.co/datasets/mahmoudsalah01/SAQR/discussions).