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-# deepwriting
-Code for `DeepWriting: Making Digital Ink Editable via Deep Generative Modeling` paper
+# DeepWriting
+Code for `DeepWriting: Making Digital Ink Editable via Deep Generative Modeling` [paper](https://arxiv.org/abs/1801.08379).
 
-In progress.
+[![Watch the video](https://img.youtube.com/vi/NVF-1csvVvc/0.jpg)](https://www.youtube.com/watch?v=NVF-1csvVvc)
+
+Implementation of conditional variational RNNs (C-VRNN).
+
+## Dataset
+We collected data from 94 authors by using [IAMOnDB](http://www.fki.inf.unibe.ch/databases/iam-handwriting-database) corpus. After discarding noisy samples of IAMOnDB, we compiled a dataset of 294 authors, fully segmented. For now, we release a very small subset of preprocessed data (i.e., test split). Full dataset in raw format will be shared when we resolve permission issues.
+
+## Pretrained Model
+1. You can download a pretrained model from [our project page](https://ait.ethz.ch/projects/2018/deepwriting/downloads/tf-1514981744-deepwriting_synthesis_model.tar.gz).
+2. Either move under `<repository_path>/runs/` or update `validation data path.` in config.json. 
+3. You can run
+```
+python tf_evaluate_hw.py -S <path_to_model_folder> -M tf-1514981744-deepwriting_synthesis_model -QL
+```
+
+
+## Dependencies
+1. Numpy
+2. Tensorflow 1.2+ (not sure if earlier versions work.)
+3. Matplotlib
+4. OpenCV (pip install opencv-python is enough)
+5. svgwrite