Update draft of model card

README.md

@@ -1,3 +1,48 @@
 ---
 license: apache-2.0
+tags:
+- Scene Text Removal
+- Image to Image
+library_name: pytorch
 ---
+
+### GaRNet
+
+This is text-removal model that introduced in the paper below and first released at [this page](https://github.com/naver/garnet). \
+[The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis](https://arxiv.org/abs/2210.07489). \
+Hyeonsu Lee, Chankyu Choi \
+Naver Corp. \
+In ECCV 2022. 
+
+### Model description
+
+GaRNet is a generator that create non-text image with given image and coresponding text box mask. It consists of convolution encoder and decoder. The encoder consists of residual block with attention module called Gated Attention.
+
+Gated Attention module has two Spatial attention branch. Each attention branch finds text stroke or its surrounding regions. The module adjusts the weight of these two domains by trainable parameters.
+
+The model was trained in PatchGAN manner with Region-of-Interest Generation. \
+The discriminator is consists of convolution encoder. Given an image, it determines whether each patch, which indicates text-box regions, is real or fake.
+All loss functions treat non-textbox regions as 'don't care'.
+
+### Intended uses & limitations
+
+This model can be used for areas that require the process of erasing text from an image, such as concealment private information, text editing.\
+You can use the raw model or pre-trained model.\
+Note that pre-trained model was trained in both Synthetic and SCUT_EnsText dataset. And the SCUT-EnsText dataset can only be used for non-commercial research purposes.
+
+### How to use
+
+You can use inference code in [this page](https://github.com/naver/garnet).
+
+### BibTeX entry and citation info
+
+```
+@inproceedings{lee2022surprisingly,
+  title={The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis},
+  author={Lee, Hyeonsu and Choi, Chankyu},
+  booktitle={European Conference on Computer Vision},
+  pages={457--472},
+  year={2022},
+  organization={Springer}
+}
+```