Update README.md

README.md

@@ -5,4 +5,65 @@ license: mit
 # FW-GAN
 
 **FW-GAN** is a frequency-aware, one-shot handwriting synthesis framework designed to produce realistic and writer-consistent handwritten text from a single reference public at [Expert Systems with Applications](https://www.sciencedirect.com/science/article/pii/S095741742503790X)
-Training code is released on [GitHub](https://github.com/DAIR-Group/FW-GAN).
+Training code is released on [GitHub](https://github.com/DAIR-Group/FW-GAN).
+
+**[FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator](https://www.sciencedirect.com/science/article/pii/S095741742503790X)**  
+Huynh Tong Dang Khoa, Dang Hoai Nam, Vo Nguyen Le Duy
+
+![test](https://github.com/DAIR-Group/FW-GAN/blob/main/docs/architecture.png?raw=true#)
+
+## Installation
+
+```console
+conda create --name fwgan python=3.10
+conda activate fwgan
+pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
+git clone https://github.com/DAIR-Group/FW-GAN.git && cd FW-GAN
+pip install -r requirements.txt
+```
+
+We provide our pretrained model weights and datasets here. For training, please download the files the files `train.hdf5` and `test.hdf5` and place them into the `data` folder. For quick evaluation, you can also download the pretrained model `FW-GAN.pth` and place it under `/data/weights/FW-GAN.pth` on the code released on [GitHub](https://github.com/DAIR-Group/FW-GAN).
+
+## Training
+
+```console
+python train.py --config ./configs/fw_gan_iam.yml
+```
+
+
+## Generate Handwtitten Text Images
+
+To generate all samples for FID evaluation you can use the following script:
+
+```console
+python generate.py --config ./configs/fw_gan_iam.yml
+```
+
+## Handwriting synthesis and reconstruction results on IAM dataset
+
+![test](https://github.com/DAIR-Group/FW-GAN/blob/main/docs/Visualization_gen.png?raw=true#)
+
+![test](https://github.com/DAIR-Group/FW-GAN/blob/main/docs/Visualization_reconstruction.png?raw=true#)
+
+## Handwriting synthesis on HANDS-VNOnDB dataset
+![test](https://github.com/DAIR-Group/FW-GAN/blob/main/docs/Visualization_Vietnamese.png?raw=true#)
+
+### Implementation details
+This work is partially based on the code released for [HiGAN](https://github.com/ganji15/HiGAN)
+
+## Citation
+If you find this work useful, please cite our paper:
+
+```bibtex
+@article{TONGDANGKHOA2026130175,
+title = {FW-GAN: Frequency-driven handwriting synthesis with wave-modulated MLP generator},
+journal = {Expert Systems with Applications},
+volume = {299},
+pages = {130175},
+year = {2026},
+issn = {0957-4174},
+doi = {https://doi.org/10.1016/j.eswa.2025.130175},
+url = {https://www.sciencedirect.com/science/article/pii/S095741742503790X},
+author = {Huynh {Tong Dang Khoa} and Dang {Hoai Nam} and Vo {Nguyen Le Duy}},
+keywords = {Handwritten text synthesis, Wavelet transform, One-shot learning, Vietnamese handwriting, Synthetic data, Generative adversarial networks},
+}