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Tahlor / simple_hwr

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BYU ML Lab Deep Integration of LM into HWR

Aanaconda

After logging in, install Anaconda 3:

cd /tmp
curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
bash Anaconda3-5.2.0-Linux-x86_64.sh

Environment

Create the environment defined in environment.yaml.

conda env create -f environment.yaml --name hwr
conda activate hwr

Configuration

All configurations are stored in the config folder as .yaml files.

Execution

Downloading/Preparing Datasets

Ensure that you have an IAM Handwriting Database access account (register), and IAM On-Line Handwriting Database access account (register), then:

cd data
./generate-all-datasets.sh

For the first IAM prompt, use your username and password for IAM Handwriting DB, then for the second IAM prompt, use your username and password for IAM On-Line Handwriting DB. This script should download/extract/setup the IAM data.

Prepping / Resampling Data

Run cd ./data_processing/online_coordinate_data && python create_dataset.py to re-format data. The training scripts expect data to be in the format that is output by this script. ** TO DO: Steamline / simplify this step, or have it done by the dataloader.

Trajectory Recovery

Modifying/updating the config files

To use existing config options, just modify the config file directly. See config/DEBUG.yaml for an example configuaration with some descriptions (though it's not guaranteed to work). The example_weights/example.conf is working with the model weights in the example_weights folder. To add new options:

  1. Add option to a config file
  2. Modify ./hwr_utils/stroke_dataset.py.py class to accept new option
  3. Modify train_stroke_recovery.py to read the option from the config file and pass to StrokeRecoveryDataset class
  4. Modify hwr_utils.py at defaults to include a default parameter in case a config file does not specify your new option.

Training

Once the data is downloaded and the environment setup, setup a config file. You should then be able to train the model:

python train_stroke_recovery.py --config PATH_TO_CONFIG

Evaluation

An example config with a model and weights can be run for offline data (though you may need to configure where your offline data is within the script).

python stroke_recovery_offline.py

Also see python stroke_recovery_online.py, which is similar but for online data.

Handwriting Recognition

Modifying/updating the config files

To use existing config options, just modify the config file directly. To add new options:

  1. Add option to a config file
  2. Modify hw_dataset.py class to accept new option
  3. Modify train.py to read the option from the config file and pass to HwDataset class
  4. Modify hwr_utils.py at defaults to include a default parameter in case a config file does not specify your new option.

Train

To train, run train.py with one of the configurations found in the configs folder. For example:

python train.py --config ./configs/baseline.yaml

Recognize

python recognize.py sample_config.json prepare_font_data/output/0.png

or 

python recognize.py sample_config_iam.json prepare_IAM_Lines/lines/r06/r06-000/r06-000-00.png

Fulton Super Computer Prerequisites

If you are a BYU student, consider requesting access to the supercomputer. Sign up here.

Next, request group access from Taylor Archibald.