Datasets:
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---
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:
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
num_bytes: 583130879
num_examples: 41245
- name: train
num_bytes: 21425838
num_examples: 1732
- name: val
num_bytes: 1090108
num_examples: 86
- name: handwritten_original
num_bytes: 512564010
num_examples: 40327
download_size: 19389645399
dataset_size: 1118210835
configs:
- 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-*
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. |