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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). | ||
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In progress. | ||
[![Watch the video](https://img.youtube.com/vi/NVF-1csvVvc/0.jpg)](https://www.youtube.com/watch?v=NVF-1csvVvc) | ||
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Implementation of conditional variational RNNs (C-VRNN). | ||
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## 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. | ||
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## 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 | ||
``` | ||
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## 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 |