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BASEDNet Technical Documentation

Table of Contents

Relevant Documentation
Commands
Manually Annotating Data
Preparing Data For Model 2
Run data.py
Run train.py
Train dhSegment (or use existing model)
Run grad_search.py
Evaluate Accuracy of Predictions

Relevant Documentation

Training dhSegment: https://hackmd.io/@ericke/B1G1fEJ9L#Training
Read The Docs: https://dhsegment.readthedocs.io/en/latest/
GitHub: https://github.com/dhlab-epfl/dhSegment
What is dhSegment: https://dhlab-epfl.github.io/dhSegment/

Commands

Check if the gpu’s are available:
nvidia-smi

Running dhSegment
View current containers + status:
docker container ls -a
If the docker container doesn’t exist, create it:

To run the docker:
docker start dhSegment
docker exec -it dhSegment bash
tensorboard --logdir .

Exit the docker using ctrl d and stop the container after use:
docker stop dhSegment

Manually Annotating Data

Transkribus Labeling Tool can be found here.

Preparing Data for Model 2

  1. Collect all good y’s (xml files) into a single folder and the images for each y into another folder
    a. For EEBO, images are at jason/datasets/EEBO and xml files are at jason/datasets/EEBO/team_page
    b. For READ_BAD, the images are in the “input” folder at jason/datasets/READ_BAD_MASTER/Train&Test Simple and xml files are in the page-gt folder
    c. Note: These folders can be found on the sakura server
  2. Run Aneesha & Lynn’s perturbation script
    a. Found in src/bad_baseline_generator.py
    b. Usage:
    python bad_baseline_generator.py [input directory] [output directory]
    c. In this case, the input directory will be the good y’s from the dataset, and the output directory will contain bad y’s with perturbations applied
  3. Label both folders separately (this step can take awhile)
    a. Found in src/label_updated.py
    b. Usage: 
    python label_updated.py [good y’s folder] [original .jpg images] [labeled_good] BASELINE  
python label_updated.py [bad y’s folder] [original .jpg images] [labeled_bad] BASELINE
    c. Should now have two folders called labeled_good and labeled_bad with .png labeled images
  4. Now, split the folders into train/val/test
    a. Download the split_folders package here
    b. Place labeled_good and labeled_bad as subdirectories of a new folder called input
    c. Run split_folder command 
    splitfolders --ratio .7 .2 .1 -- input
    d. You should now have a folder called output that contains test, train, and val data

Run data.py

  1. data.py turns the images into numpy array representations that are labeled as good or bad y’s
  2. Usage:
python data.py
  1. The input directory is the output directory from the last section that contains test, train, and val data
  2. The input directory is to be hard-coded by the individual who is programming (currently “model_data”)

Run train.py

  1. This step trains Model 2
  2. Usage:
python train.py
  1. The input directory is the output directory from the last section that contains test, train, and val numpy arrays
  2. The/model_data directory should contain 6 numpy arrays (2 Train, 2 Test, 2 Val) where each pair of numpy arrays is a pair of Training Labels and Binarized Baseline Prediction Images that have been resized to 1000x1000 pixels

Train dhSegment (or use existing model)

  1. Existing dhSegment models can be found at projects/jason/dhSegment/READ_BAD/page_modelX on the Sakura server
    a. The best-performing pre-trained dhSegment model for our purposes was arbitrarily named ‘page_model4’
  2. Details on training a new dhSegment model can be found here

Run grad_search.py

  1. This step gets the baseline predictions from the trained dhSegment and BASEDNet models
  2. Usage:
python grad_search.py
  1. The input directory is the directory that contains test numpy arrays
  2. Similar to our other scripts, the grad_search script takes in the original .jpg images from our historical document archive. Within the script, the grad_search will first generate baseline predictions from the historical document, and with these predictions, BASEDNet will provide a holistic score for the overall quality of the baseline predictions. From this, given that a set of baselines is scored as ‘bad’ by BASEDNet, we optimize the baselines over several iterations of gradient-based optimization.

Evaluate Accuracy of Predictions

  1. Download the following evaluation tool: https://github.com/Transkribus/TranskribusBaseLineEvaluationScheme
  2. Usage (from inside the target folder):
java -jar TranskribusBaseLineEvaluationScheme-0.1.3-jar-with-dependencies.jar [truth.lst] [reco.lst]
  1. The .lst files should contains the list of names of the ground truth and output predicted files