fendaq / tf-crnn

TensorFlow Convolutional Recurrent Neural Network (CRNN) using TF's Estimator

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