bartzi / handwriting-determination

Code for the Paper Synthetic Data for the Analysis of Archival Documents: Handwriting Determination

Handwriting Determination

Code for the Paper "Synthetic Data for the Analysis of Archival Documents: Handwriting Determination" - DICTA 2020.

Installation

1. make sure you have Python (>= 3.6) installed
2. clone the Repository
3. create a virtual environment
4. install all requirements with pip install -r requirements.txt
5. You should be ready to go!

Docker

You can also use Docker to run the project:

1. clone the repository
2. run docker build -t <name> .
3. done

Data Preparation

We provide our data generation tool (in the data_generation directory) and also information on how to get all base data that we used for generation of our train dataset. You can find more information on that here.

Before you can generate data, you first have to adapt the file data_generation/generation_profiles.py and set the base path to all auxiliary files (line 7). Generation can then be started by running the script data_generation/data_generator.py. You can check possible option by running

python data_generator.py -h

You can also download the training data that we used for the training of our model here.

Training of a Model

You can train a model by getting/preparing the training data.

Before training you'll have to configure everything. In the section [PATHS] you have to set the paths to your train.json and validation.json.

You can then use the script train_handwriting_determination.py to train a model. It could look like this:

python train_handwriting_determination.py training config.cfg --gpu 0

This runs the training, saves the logs in the directory test/training, while setting the config to config.cfg and running the training on GPU 0.

Further options are available:

• --log-dir: change the directory where logs are saved
• --gpu: set the gpu id to use (negative valuse indicate CPU)
• --resume: provide path to a log dir and allow training to resume from last saved trainer snapshot.
• --save-gradient: plots gradients into tensorboard.

You can also inspect the train logs, using tensorboard.

Evaluation

After you've trained a model, you can evaluate the model using the script evaluate.py. You can use it like this (assuming that our model was logged to test/training and your data is in /data/val.json):

python evaluate.py test/training HandwritingNet_ /data/val.json -g 0

This evaluates all trained model files that start with HandwritingNet_ in the directory test/training using the validation groundtruth in /data/val.json.

Further options are possible:

• --log-name: if you changed the default name of the log json, you need to set this here
• -g, --gpu: GPU id to use (same semantics as with training)
• -b, --batch-size: batch size to use for evaluation (batch size of 1 is recommended)
• -e, --evaluation-name: name of the resulting json file, where evaluation results are saved. Useful if you want to run evaluation on multiple datasets.
• --force-reset: force the script to run the full dataset, in case a result file already exists
• -r, --render-regions: plot decisions of the model for each inout image. Choices allow you to specify whether to plot, e.g., false positives, true positives, or all results.
• --render-negatives: render regions only renders the confidence for positive decisions, if you want to render confidence for negative decisions, use this flag.

Analysis of Multiple Images

If you want to analyse whether images contain handwriting or not, you can use the script analyse_images.py in the training directory. You can run it like so:

python analyse_images.py /data/images /data/model/HandwritingNet_250000.npz

Where /data/images is the path to the dir where your images are and /data/model/HandwritingNet_250000.npz is the path of the model you want to evaluate. You can check further options with -h. Please note, this script only works with a GPU in your server and automatically determines the number of GPUs it uses. If you want to control the GPUs used by the script, just set the environment variable CUDA_VISIBLE_DEVICES.

Citation

If you think our work is useful, please consider citing it:

@article{bartzsynthetic,
  title={Synthetic Data for the Analysis of Archival Documents: Handwriting Determination},
  author={Bartz, Christian and Seidel, Laurenz and Nguyen, Duy-Hung and Bethge, Joseph and Yang, Haojin and Meinel, Christoph}
  booktitle={2020 Digital Image Computing: Techniques and Applications (DICTA)}
}

Questions and Enhancements

If you have any questions or you have something nice to contribute, please let us know by opening an issue or pull request.