Implementation of a Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition tasks, such as scene text recognition and OCR.
This implementation is based on Tensorflow 2.0 and uses tf.keras and tf.data modules to build the model and to handle input data.
To access the previous version implementing Shi et al. paper, go to the v.0.5.2 tag.
tf_crnn makes use of tensorflow-gpu package (so CUDA and cuDNN are needed).
You can install it using the environment.yml file provided and use it within an environment.
conda env create -f environment.yml
See also the docs for more information.
Train a model with IAM dataset.
Create an account
Create an account on the official IAM dataset page in order to access the data. Export your credentials as enviornment variables, they will be used by the download script.
export IAM_USER=<your-username>
export IAM_PWD=<your-password>
Generate the data in the correct format
cd hlp
python prepare_iam.py --download_dir ../data/iam --generated_data_dir ../data/iam/generated
cd ..
Train the model
python training.py with config.json
More details in the documentation.