README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 40088927784
    num_examples: 30000
  download_size: 42113504357
  dataset_size: 40088927784
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
language:
- ar
---
# Arabic Salary Report OCR Dataset — Mixed Numeric Formats

## 📄 Overview
The **Arabic Salary Report OCR Dataset** is a synthetic dataset of **30,000 images** designed for training and evaluating OCR systems on **Arabic text containing numeric data**.  
It incorporates variations in numeric representation, including both **Arabic-Indic numerals** (٠١٢٣٤٥٦٧٨٩) and **Western numerals** (0–9), embedded in realistic salary report layouts.

This dataset is ideal for:
- Fine-tuning OCR models to recognize **Arabic salary reports**.
- Handling **mixed-language numeric formats**.
- Benchmarking Arabic financial document parsing.

---

## 📦 Dataset Composition
The dataset contains **30,000 images** split into two main structural formats:

| Format Type         | Quantity | Description |
|---------------------|----------|-------------|
| **Table format**    | 15,000   | Salary figures embedded inside structured tables. |
| **Paragraph format**| 15,000   | Salary figures integrated into continuous Arabic text paragraphs. |

Each format has an even split of numeric styles:
- **50% Arabic-Indic numerals** only.  
- **50% Mixed numerals** (combination of Arabic-Indic and Western).

All text content is **entirely in Arabic**, except for the Western numerals in the mixed format.

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## 🛠 Data Generation & Purpose
The dataset was **synthetically generated** to simulate realistic salary reports, ensuring:
- Variation in font styles, sizes, and layouts.
- Presence of both structured (tables) and unstructured (paragraphs) salary data.
- Representation of both numeric systems to improve OCR model robustness.

---

## 🔍 Example Use Cases
- Training OCR models to handle **Arabic text with mixed numerals**.
- Fine-tuning language models for **salary extraction** from financial documents.
- Benchmarking document understanding systems for **Arabic financial reports**.

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## 📥 Usage
To load the dataset in Python with Hugging Face `datasets`:
```python
from datasets import load_dataset

dataset = load_dataset("moekh/new-digit-ocr-dataset")
print(dataset)