Convolutional Recurrent Neural Network in Tensorflow (tf.crnn)

CRNN model in Tensorflow using Estimators

Implementation of the Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition tasks, such as scene text recognition and OCR. Original paper http://arxiv.org/abs/1507.05717 and code https://github.com/bgshih/crnn

This version uses the tf.estimator.Estimator to build the model.

Contents

• src/model.py : definition of the model
• src/data_handler.py : functions for data loading, preprocessing and data augmentation
• src/config.py : class Params manages parameters of model and experiments
• src/decoding.py : helper fucntion to transform characters to words
• train.py : script to launch for training the model, more info on the parameters and options inside
• export_model.py: script to export a model once trained, i.e for serving
• Extra : hlp/numbers_mnist_generator.py : generates a sequence of digits to form a number using the MNIST database
• Extra : hlp/csv_path_convertor.py : converts a csv file with relative paths to a csv file with absolute paths

How to train a model

The main script to launch is train.py. To train the model, you should input a csv file with each row containing the filename of the image (full path) and its label (plain text) separated by a delimiting character (let's say ';') :

/full/path/to/image1.{jpg,png};string_label1
/full/path/to/image2.{jpg,png};string_label2
...

For example launch the script using :

python train.py -g 1 -ft train_data.csv -fe val_data.csv -o ./export_model_dir

See train.py for more details on the options.

Dependencies

• tensorflow (1.3)
• tensorflow-tensorboard (0.1.7) (not mandatory but useful to visualise loss, accuracy and inputs / outputs)
• tqdm for progress bars
• json
• glob