Training an OCR

Data format

The DD platform has the following requirements for training from images for OCR:

  • All data must be in image format, most encoding supported (e.g. png, jpg, …)
  • A main text file list all images path and their OCR string counterpart, using space as a separator. See on the right for data format and examples

  • You need to prepare both a train.txt and test.txt file for training and testing purposes.

DD platform comes with a custom Jupyter UI that allows testing your OCR dataset prior to training:

Image segmentation data check in DD platform Jupyter UI

Training an OCR reader

  • OCR image & data example

Example OCR image

  • OCR example train.txt file:

    /opt/platform/examples/word_mnist/v011_words_small/9281.png possible
    /opt/platform/examples/word_mnist/v011_words_small/3426.png burgers,
    /opt/platform/examples/word_mnist/v011_words_small/4901.png bank.
    /opt/platform/examples/word_mnist/v011_words_small/8.jpeg what
    /opt/platform/examples/word_mnist/v011_words_small/1547.jpeg OF
    /opt/platform/examples/word_mnist/v011_words_small/2238.jpeg Littlefield

Using the DD platform, from a JupyterLab notebook, start from the code on the right.

OCR notebook snippet:

ocr = OCR(
    'word_mnist',
    training_repo='/opt/platform/examples/word_mnist/train.txt',
    testing_repo='/opt/platform/examples/word_mnist/test.txt',
    host='deepdetect_training',
    port=8080,
    img_height=80,
    img_width=128,
    align=False,
    model_repo='/opt/platform/models/training/jolibrain/words_mnist',
    nclasses=100,
    template='crnn',
    iterations=10000,
    test_interval=1000,
    snapshot_interval=1000,
    batch_size=128,
    test_batch_size=32,
    noise_prob=0.001,
    distort_prob=0.001,
    gpuid=1,
    base_lr=0.0001,
    solver_type='ADAM',
    mirror=False,
    rotate=False,
    resume=False
)
ocr

This prepares a CNN + dual-LSTM layers deep network with the following parameters:

  • word_mnist is the example job name
  • training_repo specifies the location of the data