---
language:
- ar
configs:
- config_name: default
  data_files:
  - split: Amiri
    path: Amiri/*.csv
  - split: Sakkal_Majalla
    path: Sakkal_Majalla/*.csv
  - split: Arial
    path: Arial/*.csv
  - split: Calibri
    path: Calibri/*.csv
  - split: Scheherazade_New
    path: Scheherazade_New/*.csv
features:
  text:
    dtype: string
tags:
- dataset
---

### Dataset Description

This dataset is designed for training and evaluating Optical Character Recognition (OCR) models 
for Arabic text. It is an extension of an open-source dataset and includes text rendered in multiple Arabic fonts (Amiri, Sakkal Majalla, Arial, Calibri and Scheherazade New). 
The dataset simulates real-world book layouts to enhance OCR accuracy.

### Dataset Structure
The dataset is divided into five splits based on font name (Sakkal_Majalla, Amiri, Arial, Calibri, and Scheherazade_New). 
Each split contains data specific to a single font. Within each split, the following attributes are present:
- **image_name**: Unique identifier for each image.
- **chunk**: The text content associated with the image.

- **font_name**: The font used in text rendering.

- **image_base64**: Base64-encoded image representation.

### How to Use

```python
from datasets import load_dataset
import base64
from io import BytesIO
from PIL import Image
# Load dataset with streaming enabled
ds = load_dataset("xya22er/text_to_image", streaming=True)
print(ds)




# Load the dataset

# Iterate over a specific font dataset (e.g., Amiri)
for sample in ds["Amiri"]:
    image_name = sample["image_name"]
    chunk = sample["chunk"]  # Arabic text transcription
    font_name = sample["font_name"]
    
    # Decode Base64 image
    image_data = base64.b64decode(sample["image_base64"])
    image = Image.open(BytesIO(image_data))

    # Show the image (optional)
    image.show()

    # Print the details
    print(f"Image Name: {image_name}")
    print(f"Font Name: {font_name}")
    print(f"Text Chunk: {chunk}")
    
    # Break after one sample for testing
    break
```

# OCR Dataset Generation Pipeline
To create your own dataset, you can use the following repository: [text2image](https://github.com/riotu-lab/text2image).