Datasets:
Arabic Salary Report OCR Dataset β Mixed Numeric Formats
π 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 (Ω Ω‘Ω’Ω£Ω€Ω₯Ω¦Ω§Ω¨Ω©) and Western numerals (0β9), 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.
π¦ 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.
π 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.
π 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.
π₯ Usage
To load the dataset in Python with Hugging Face datasets
:
from datasets import load_dataset
dataset = load_dataset("moekh/new-digit-ocr-dataset")
print(dataset)
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