printed-hw-segmentation is a tool that allows segmentation (identification) of printed and handwritten text using a fully convolutional network with CRF post-processing.

If you make use of this work (the corresponding paper can be found here), please cite it as:

@article{Dutly2019PHTIWSAP,
  title={PHTI-WS: A Printed and Handwritten Text Identification Web Service Based on FCN and CRF Post-Processing},
  author={Nicolas Dutly and Fouad Slimane and Rolf Ingold},
  journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
  year={2019},
  volume={2},
  pages={20-25}
}

left: input image, middle: raw FCN output, right: output after CRF postprocessing

With each release a docker image of the code is published on docker hub. This image can be used in conjuction with DIVA services to provide segmenation as a web service. It can also be used locally.

The web service version of this project can be tested at the following link

After pulling (docker pull jumpst3r/printed-hw-segmentation:latest) the image, the model can be applied to an image with

docker run -it --rm -v /FULL_PATH_TO/example.png:/input/example.png -v /FULL_PATH_TO_OUTPUT_FOLDER/:/output/ jumpst3r/printed-hw-segmentation sh /input/script.sh /input/example.png /output/

The resulting image will be saved in the output path provided by the user

Follow the guide here to install the DIVA services framework on a server.

Once install, our method can be installed by make a POST request to the alogithms endpoint. This request should contain the JSON file install.json located in the diva-service folder of this repository. This will return a link pointing to the newly created method. The JSON file upload.json can then be run to upload an image to the server by making a request to that link. This will return an ID for the uploaded image. Finally the method can be executed using the provided ID and the JSON file run.json

If you wish to modify or try to execute individual files, create a conda new conda environment with the file environment.yml. After activating the newly created environment, you should be able to run and modify existing files without dependency problems.

A few important files are listed bellow.

The FCN/ folder contains all files which are relevant to the training and testing of the two FCN models fcn-light1 and fcn-light-2.

Specifically, the models are defined in the files fcn-light-1.py and fcn-light-2.py.

If you want to test the performance and get IoU result metrics, you will need access to the test/ folder of the printed-hw-seg dataset (upon request).

To classify single images you can use the script classifier_fcnn.py, which offers a bunch of options. The release binary is a standalone version of this file.

The fontend/ folder contains a discontinued frontend for interations with the DIVA service framework.

The docker/ folder contains docker-specific files which are used to generate the docker image of the tool.

The diva-service/ folder contains example JSON file which can be used to deploy the method on a server running DIVA services.

disclaimer: The quality of the predictions depends on many factors such as input resolutions, format, colors etc. We do not claim that the models present in this repository perform universally well on all documents. The models were train, validated and tested on a private dataset.

For word level segmentation, have a look at the practical-word-level branch.