README.md

---
language:
- en
- vi
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- image-to-image
pretty_name: NomGenie - Font Diffusion for Sino-Nom Language
dataset_info:
- config_name: comparison
  features:
  - name: character
    dtype: string
  - name: style
    dtype: string
  - name: font
    dtype: string
  - name: content_image
    dtype: image
  - name: style_image
    dtype: image
  - name: target_image
    dtype: image
  - name: comparison_image
    dtype: image
  - name: content_hash
    dtype: string
  - name: target_hash
    dtype: string
  splits:
  - name: handwritten_original
    num_bytes: 9235671096
    num_examples: 75512
  - name: handwritten_val
    num_bytes: 125036866
    num_examples: 943
  - name: handwritten_train
    num_bytes: 7186472967
    num_examples: 59472
  download_size: 14752106451
  dataset_size: 16547180929
- config_name: default
  features:
  - name: character
    dtype: string
  - name: style
    dtype: string
  - name: font
    dtype: string
  - name: content_image
    dtype: image
  - name: target_image
    dtype: image
  - name: content_hash
    dtype: string
  - name: target_hash
    dtype: string
  splits:
  - name: train_original
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    num_examples: 41245
  - name: train
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    num_examples: 1732
  - name: val
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  - name: handwritten_original
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    num_examples: 75512
  - name: handwritten_train
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    num_examples: 59472
  - name: handwritten_val
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    num_examples: 943
  download_size: 30344615849
  dataset_size: 2353794941
- config_name: fourier
  features:
  - name: character
    dtype: string
  - name: style
    dtype: string
  - name: font
    dtype: string
  - name: content_image
    dtype: image
  - name: style_image
    dtype: image
  - name: target_image
    dtype: image
  - name: comparison_image
    dtype: image
  - name: content_hash
    dtype: string
  - name: target_hash
    dtype: string
  splits:
  - name: total
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    num_examples: 27960
  download_size: 897988686
  dataset_size: 1254547987
- config_name: streaming
  features:
  - name: character
    dtype: string
  - name: style
    dtype: string
  - name: font
    dtype: string
  - name: content_image
    dtype: image
  - name: style_image
    dtype: image
  - name: target_image
    dtype: image
  - name: comparison_image
    dtype: image
  - name: content_hash
    dtype: string
  - name: target_hash
    dtype: string
  splits:
  - name: train
    num_bytes: 2794896921
    num_examples: 61236
  - name: val
    num_bytes: 33483349
    num_examples: 752
  - name: total
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    num_examples: 75560
  download_size: 36084952999
  dataset_size: 6275047388
configs:
- config_name: comparison
  data_files:
  - split: handwritten_original
    path: comparison/handwritten_original-*
  - split: handwritten_val
    path: comparison/handwritten_val-*
  - split: handwritten_train
    path: comparison/handwritten_train-*
- config_name: default
  data_files:
  - split: train_original
    path: data/train_original-*
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: handwritten_original
    path: data/handwritten_original-*
  - split: handwritten_train
    path: data/handwritten_train-*
  - split: handwritten_val
    path: data/handwritten_val-*
- config_name: fourier
  data_files:
  - split: total
    path: fourier/total-*
- config_name: streaming
  data_files:
  - split: total
    path: streaming/total-*
  - split: val
    path: streaming/val-*
  - split: train
    path: streaming/train-*
tags:
- font
- diffusion
- deep-learning
- computer-vision
---

# NomGenie: Font Diffusion for Sino-Nom Language

**NomGenie** is a specialized image-to-image dataset designed for font generation and style transfer within the **Sino-Nom (Hán-Nôm)** script system. This dataset facilitates the training of deep learning models—particularly Diffusion Models and GANs—to preserve the historical and structural integrity of Vietnamese Nom characters while applying diverse typographic styles.

## Dataset Description

The dataset consists of paired images: a **content image** (representing the skeletal or standard structure of a character) and a **target image** (representing the character rendered in a specific artistic or historical font style).

### Key Features
* **character**: The specific Sino-Nom character represented.
* **style/font**: Metadata identifying the aesthetic transformation applied.
* **content_image**: The source glyph used as the structural reference.
* **target_image**: The ground truth stylized glyph for model supervision.
* **Hashing**: `content_hash` and `target_hash` are provided to ensure data integrity and assist in deduplication.

## Dataset Structure

### Data Splits
The dataset is organized into three distinct splits to support various training stages:

| Split | Examples | Size | Description |
| :--- | :--- | :--- | :--- |
| **train_original** | 8,235 | 124.79 MB | The full original training set. |
| **train** | 5,172 | 79.72 MB | A curated subset optimized for standard training. |
| **val** | 318 | 4.48 MB | Validation set for hyperparameter tuning and evaluation. |

## Quick Start

To use this dataset with the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("path/to/NomGenie")

# Access a training sample
sample = dataset['train'][0]
display(sample['content_image'])
display(sample['target_image'])

## Technical Details
- Task Category: image-to-image

- Languages: Vietnamese (vi), English (en)

- License: Apache 2.0

- Primary Use Case: Generative AI for cultural heritage preservation and digital typography.