Paired Image to Image Translation for Strikethrough Removal From Handwritten Words

Raphaela Heil✉️, Ekta Vats and Anders Hast

Code for the DAS 2022 paper "Paired Image to Image Translation for Strikethrough Removal From Handwritten Words"

Table of Contents

1. Code
   1. Train
   2. Test
2. Data
3. Citation
4. Acknowledgements

1 Code

1.1 Train

python -m src.train -file <path to config file> -section <section name>

1.2 Test

python -m src.test -file <path to config file> -data <path to test data>

If you want to use a checkpoint with a different name than best_fmeasure.pth add: -checkpoint <filename> and if you want to save the model outputs, i.e. cleaned images, add the flag -save

Data

• IAM_synth: Synthetic strikethrough dataset
  • Zenodo: https://doi.org/10.5281/zenodo.4767094
  • based on the IAM database
  • multi-writer
  • generated using https://doi.org/10.5281/zenodo.4767062
• Dracula_real: Genuine strikethrough dataset
  • Zenodo: https://doi.org/10.5281/zenodo.4765062
  • single-writer
  • blue ballpoint pen
  • clean and struck word images registered based on:

    J. Öfverstedt, J. Lindblad and N. Sladoje, "Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information," in IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3584-3597, July 2019, doi: 10.1109/TIP.2019.2899947. (Paper, Code)

• Dracula_synth: Synthetic single-write dataset
  • Zenodo: https://doi.org/10.5281/zenodo.6406538
  • based on the train split of Dracula_real
  • five partitions with different strikethrough strokes applied to each word

3 Citation

DAS 2022

@INPROCEEDINGS{heil2022strikethrough,
  author={Heil, Raphaela and Vats, Ekta and Hast, Anders},
  booktitle={15TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS (DAS 2022)},
  title={{Paired Image to Image Translation for Strikethrough Removal From Handwritten Words}},
  year={2022},
  pubstate={to appear}}

4 Acknowledgements

The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Chalmers Centre for Computational Science and Engineering (C3SE) partially funded by the Swedish Research Council through grant agreement no. 2018-05973. This work is partially supported by Riksbankens Jubileumsfond (RJ) (Dnr P19-0103:1).