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--- |
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language: |
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- en |
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- vi |
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license: apache-2.0 |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- image-to-image |
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pretty_name: NomGenie - Font Diffusion for Sino-Nom Language |
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dataset_info: |
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features: |
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- name: character |
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dtype: string |
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- name: style |
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dtype: string |
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- name: font |
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dtype: string |
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- name: content_image |
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dtype: image |
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- name: target_image |
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dtype: image |
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- name: content_hash |
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dtype: string |
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- name: target_hash |
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dtype: string |
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splits: |
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- name: train_original |
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num_bytes: 583130879 |
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num_examples: 41245 |
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- name: train |
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num_bytes: 21425838 |
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num_examples: 1732 |
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- name: val |
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num_bytes: 1090108 |
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num_examples: 86 |
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- name: handwritten_original |
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num_bytes: 512564010 |
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num_examples: 40327 |
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download_size: 19389645399 |
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dataset_size: 1118210835 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train_original |
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path: data/train_original-* |
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- split: train |
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path: data/train-* |
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- split: val |
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path: data/val-* |
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- split: handwritten_original |
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path: data/handwritten_original-* |
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tags: |
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- font |
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- diffusion |
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- deep-learning |
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- computer-vision |
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--- |
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# NomGenie: Font Diffusion for Sino-Nom Language |
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**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. |
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## Dataset Description |
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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). |
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### Key Features |
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* **character**: The specific Sino-Nom character represented. |
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* **style/font**: Metadata identifying the aesthetic transformation applied. |
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* **content_image**: The source glyph used as the structural reference. |
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* **target_image**: The ground truth stylized glyph for model supervision. |
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* **Hashing**: `content_hash` and `target_hash` are provided to ensure data integrity and assist in deduplication. |
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## Dataset Structure |
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### Data Splits |
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The dataset is organized into three distinct splits to support various training stages: |
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| Split | Examples | Size | Description | |
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| :--- | :--- | :--- | :--- | |
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| **train_original** | 8,235 | 124.79 MB | The full original training set. | |
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| **train** | 5,172 | 79.72 MB | A curated subset optimized for standard training. | |
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| **val** | 318 | 4.48 MB | Validation set for hyperparameter tuning and evaluation. | |
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## Quick Start |
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To use this dataset with the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("path/to/NomGenie") |
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# Access a training sample |
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sample = dataset['train'][0] |
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display(sample['content_image']) |
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display(sample['target_image']) |
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## Technical Details |
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- Task Category: image-to-image |
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- Languages: Vietnamese (vi), English (en) |
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- License: Apache 2.0 |
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- Primary Use Case: Generative AI for cultural heritage preservation and digital typography. |
