The official GitHub repository for the paper on DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis

#### The work will feature in 16th ICDAR 2021 (Lausanne, Switzerland). ### Getting Started #### Step 1: Clone this repository and change directory to repository root ```bash git clone https://github.com/biswassanket/synth_doc_generation.git cd synth_doc_generation ``` #### Step 2: Make sure you have conda installed. If you do not have conda, here's the magic command to install miniconda. ```bash curl -o ./miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh chmod +x ./miniconda.sh ./miniconda.sh -b -u -p . ``` #### Step 3: Create an environment to run the project and install required dependencies. * To create **conda** environment: `conda env create -f environment.yml` #### Step 4: Activate the conda environment ```bash conda activate layout2im ``` #### Step 5: Downloading Dataset * To download **PubLayNet** dataset: `curl -o /publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz` #### Step 5: Downloading the trained models * Download the trained models to `checkpoints/pretrained/`. 1. [trained model on PubLayNet](https://drive.google.com/file/d/1_yaxaXEINqAD-jlSqxtz1yIsor_0Lwam/view?usp=sharing) #### Step 6: Testing Testing on PubLayNet dataset: ```bash $ python layout2im/test.py --dataset publaynet --coco_dir datasets/publaynet \ --saved_model checkpoints/pretrained/publaynet_netG.pkl \ --results_dir checkpoints/pretrained_results_publaynet ``` #### Step 7: Training ```bash $ python layout2im/train.py ``` ### Results #### 1. T-SNE Visualization of Synthetic Document Images

#### 2. Diverse results generated from the same document layout

#### 3. Examples of interactive generation of documents by the user

### Citation If you find this code useful in your research then please cite ``` @inproceedings{biswas2021docsynth, title={DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis}, author={Biswas, Sanket and Riba, Pau and Llad{\'o}s, Josep and Pal, Umapada}, booktitle={International Conference on Document Analysis and Recognition (ICDAR)}, year={2021} } ``` ### Acknowledgement Our project has adapted and borrowed the code structure from [layout2im](https://github.com/zhaobozb/layout2im). We thank the authors. This research has been partially supported by the Spanish projects RTI2018-095645-B-C21, and FCT-19-15244, and the Catalan projects 2017-SGR-1783, the CERCA Program / Generalitat de Catalunya and PhD Scholarship from AGAUR (2021FIB-10010). ### Authors * [Sanket Biswas](https://github.com/biswassanket) * [Pau Riba](https://github.com/priba) ### Conclusion Thank you and sorry for the bugs!