---
license: apache-2.0
language:
- ar
tags:
- OCR
- Arabic-OCR
- OCR-Arabic-HTML

configs:
  - config_name: default
    data_files:
      - split: train
        path: metadata.csv

---

# Gutenberg Arabic HTML-Page Dataset

## 📖 Dataset Description

The **Gutenberg Arabic HTML-Page Dataset** is a large-scale, synthetically generated dataset designed for training and evaluating document understanding and Optical Character Recognition (OCR) models. The primary goal of this project is to provide a comprehensive resource of page images paired with their corresponding structured HTML ground truth, with a focus on the Arabic language.

The dataset was created by processing classic English literature from the **Project Gutenberg** library. The content was automatically translated into Arabic and then rendered into thousands of unique page images, featuring a wide variety of layouts, fonts, and sizes to ensure model robustness.

This resource is ideal for tasks such as:
* OCR for non-standard Arabic fonts.
* Document layout analysis.
* Training models to understand the semantic structure of a page (e.g., headers, paragraphs).

---

## 🛠️ Dataset Creation

The creation process followed a multi-stage automated pipeline:

### 1. Source Data Acquisition
A selection of popular books was downloaded in HTML format from the Project Gutenberg library. This provided the base text and initial document structure.

### 2. Automated Translation
The raw English HTML content of each book was parsed, and all text nodes were translated into Arabic using the **Google Translate API** (via the `deep-translator` library). This step preserved the original HTML tags while replacing the text content.

### 3. Page Segmentation and Rendering
The translated HTML books were too large to be used as single data points. Therefore, a script using **Playwright** was developed to intelligently segment the content into individual pages. This process works by:
* Virtually rendering the book in a headless browser.
* Measuring the content height as text is added.
* "Cutting" the page once it reaches the maximum height for a given layout.

### 4. Synthetic Data Augmentation
To create a diverse and robust dataset, several augmentation techniques were applied during the rendering phase:
* **Page Layouts:** Pages were rendered using multiple dimensions and aspect ratios.
    * A4 (794x1123 px)
    * Letter (816x1056 px)
    * Small (600x850 px)
* **Typography:** A variety of high-quality, open-source Arabic fonts were used randomly for each page to ensure models do not overfit to a single style.
    * **Fonts Used:** Noto Naskh Arabic, Amiri, Tajawal, Cairo, Lateef, IBM Plex Sans Arabic.
    * **Font Sizes:** Ranged from 14pt to 24pt.

### 5. Structural Heuristics
A key feature of this dataset is its structured ground truth. Simple heuristics were applied to the HTML of each page to automatically add basic semantic tags, better reflecting a real-world document structure.
* Short, standalone lines at the start of a page were wrapped in `<header><h1>...</h1></header>`.
* All page content was enclosed within `<main><section>...</section></main>` tags.

---

## 📊 Dataset Statistics

The current version of the dataset consists of **1,316 pages** generated from 16 distinct books.

### Included Books
| Book Title                              | Gutenberg ID | Page Count |
| --------------------------------------- | ------------ | ---------- |
| Pride and Prejudice                     | 1342         | 629        |
| The Adventures of Tom Sawyer            | 74           | 333        |
| A Tale of Two Cities                    | 23 & 98      | 70         |
| The Adventures of Huckleberry Finn      | 76           | 48         |
| Anne of Green Gables                    | 45           | 42         |
| The War of the Worlds                   | 36           | 32         |
| Frankenstein                            | 84           | 31         |
| The Wonderful Wizard of Oz              | 55           | 28         |
| A Christmas Carol                       | 46           | 20         |
| Peter Pan                               | 16           | 20         |
| Through the Looking-Glass               | 12           | 17         |
| Alice's Adventures in Wonderland        | 11           | 15         |
| The Adventures of Sherlock Holmes       | 1661         | 15         |
| The Strange Case of Dr. Jekyll and Mr. Hyde | 43           | 13         |
| The Legend of Sleepy Hollow             | 41           | 3          |
| **Total** | **-** | **1,316** |

### Exploratory Data Analysis (EDA)
An analysis of the generated HTML files reveals the following distribution of key structural tags:

| Tag       | Total Occurrences | Pages with Tag | Percentage of Pages |
| --------- | ----------------- | -------------- | ------------------- |
| `<main>`    | 1,316             | 1,316 / 1,316  | 100.00%             |
| `<section>` | 1,316             | 1,316 / 1,316  | 100.00%             |
| `<p>`       | 21,212            | 1,260 / 1,316  | 95.74%              |
| `<h1>`      | 194               | 180 / 1,316    | 13.68%              |
| `<header>`  | 178               | 178 / 1,316    | 13.53%              |
| `<footer>`  | 0                 | 0 / 1,316      | 0.00%               |

This shows that every page has a consistent base structure (`<main>`, `<section>`), and a significant portion (~13.5%) were identified as having headers. The absence of `<footer>` tags is an artifact of the simple heuristics used, which could be expanded in future versions.

---

## 📁 Dataset Structure

The dataset is organized into three main components:
* `/images`: A folder containing all rendered PNG page images.
* `/html`: A folder containing the corresponding structured HTML ground truth files.
* `metadata.csv`: A manifest file that links each image to its HTML file. It contains two columns: `image_path` and `html_path`.

## 🚀 How to Use

You can easily load and work with this dataset using the `datasets` library.

```python
from datasets import load_dataset
from PIL import Image
import requests
from io import BytesIO

# Load the dataset from the Hugging Face Hub
dataset = load_dataset("FatimahEmadEldin/Gutenberg-Arabic-OCR-HTML-Pages")

# Function to fetch and process the data
def get_example(index):
    example = dataset['train'][index]
    
    # The image_path is now a PIL Image object automatically loaded by datasets
    image = example['image_path']
    
    # The html_path is now the string content of the HTML file
    html_content = example['html_path']
    
    return image, html_content

# Get the first example
image, html = get_example(0)

print("Displaying Image:")
display(image)

print("\nCorresponding HTML:")
print(html)
```


# Citing the Dataset

If you use this dataset in your research, please consider citing it:


```python

@misc{EmadEldin_2025_gutenberg_arabic,
  author = {Fatimah Emad Eldin},
  title = {Gutenberg Arabic HTML-Page Dataset},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{[https://huggingface.co/datasets/FatimahEmadEldin/Gutenberg-Arabic-OCR-HTML-Pages](https://huggingface.co/datasets/FatimahEmadEldin/Gutenberg-Arabic-OCR-HTML-Pages)}},
}

```