This collection demonstrates how to construct and train a deep, bidirectional stacked LSTM using a CNN features as input with CTC loss to perform robust word recognition. The model is a straightforward adaptation of Shi et al.'s CRNN architecture (arXiv:1507.0571). The provided code downloads and trains using Jaderberg et al.'s synthetic data (IJCV 2016).

Developed for Tensorflow 1.1

The model as build is a hybrid of Shi et al.'s CRNN architecture (arXiv:1507.0571) and the VGG deep convnet, which reduces the number of parameters by stacking pairs of small 3x3 kernels. In addition, the pooling is also limited in the horizontal direction to preserve resolution for character recognition. There must be at least one horizontal element per character.

Assuming one starts with a 32x32 image, the dimensions at each level of filtering are as follows:

To accelerate training, a batch normalization layer is included before each pooling layer and ReLU non-linearities are used throughout. Other model details should be easily identifiable in the code.

The default training mechanism uses the ADAM optimizer with learning rate decay.

To completely train the model, you will need to download the mjsynth dataset, pack it into sharded tensorflow records. Then you can start the training process, a tensorboard monitor, and an ongoing evaluation thread. The individual commands are packaged in the accompanying Makefile.

make mjsynth-download
make mjsynth-tfrecord
make train &
make monitor &
make test

To monitor training, point your web browser to the url (e.g., (http://127.0.1.1:8008)) given by the Tensorboard output.

Note that it may take 4-12 hours to download the complete mjsynth data set. A very small set (0.1%) of packaged example data is included; to run the small demo, skip the first two lines involving mjsynth.

With a Geforce GTX 1080, the demo takes about 20 minutes for the validation character error to reach 45% (using the default parameters); at one hour (roughly 7000 iterations), the validation error is just over 20%.

With the full training data, by one million iterations the model typically converges to around 7% training character error and 35% word error, both varying by 2-5%.

The test script streams statistics for small batches of validation (or test) data. It ouputs the label error (percentage of characters predicted incorrectly), the test loss, and the sequence error (percentage of words--entire sequences--predicted incorrectly.)

There are many command-line options to configure training parameters. Run train.py or test.py with the --help flag to see them or inspect the scripts. Model parameters are not command-line configurable and need to be edited in the code (see model.py).