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Tensorflow implementation of handwritten sequense of small letter recognition.

  • The handwritten dataset used is IAM
  • The wrapper for code was taken from youtube-8m contest.
  • The lstm2d.py and lstm1d.py was taken from Tensorflow contrib.
  • The input from training and testing are stored in tfrecods files.

Dependencies

  • Python 2.7
  • Tensorflow 1.1
  • Note: I used that docker image in order to run the project, change the tensorflow vertion to 1.1.0

In order to make tfrecords files

  • open make tfRecods.ipynb notebook
  • change the path '../xml/' and '../forms/' to the path to the xml and forms folders from IAM dataset
  • run all the cells on the notebook
  • in the 'test-batch1' folder will be created tfrecords files
  • note: sorted(glob.glob(pathXML+"*.xml"))[:200] will process just 200 images, change 200 to more
  • note: this notebook will create tf.records with images of shape: (350,25)

In order to run the training with multidimentional lstm:

python train.py --slices 55 --width 12 --stride 1  --Bwidth 350 --vocabulary_size 29 \
--height 25 --train_data_pattern test-batch1/handwritten-test-{}.tfrecords --train_dir models-feds \
--test_data_pattern test-batch1/handwritten-test-{}.tfrecords  --max_steps 20 --batch_size 20 --beam_size 1 \
--input_chanels 1 --start_new_model --rnn_cell LSTM --model MDLSTMCTCModel --num_epochs 6000

In order to run the training with slice-image lstm:

python train.py --slices 55 --width 12 --stride 1  --Bwidth 350 --vocabulary_size 29 \
--height 25 --train_data_pattern test-batch1/handwritten-test-{}.tfrecords --train_dir models-feds \
--test_data_pattern test-batch1/handwritten-test-{}.tfrecords  --max_steps 20 --batch_size 20 --beam_size 1 \
--input_chanels 1 --start_new_model --rnn_cell LSTM --model LSTMCTCModel --num_epochs 6000

In order to run the inference with slice-image lstm:

python inference.py --slices 55 --width 12 --Bwidth 350 --stride 1 \
    --input_chanels 1 --height 25 --input_data_pattern  test-batch1/handwritten-test-1.tfrecords \
    --train_dir models-feds  --batch_size 20 --beam_size 1

options for training

  • --slices: number of slices
  • --width: width of the window
  • --stride: step for
  • --Bwidth: image width
  • --train_data_pattern ../tf-data/handwritten-test-{}.tfrecords
  • --train_dir separable_lstm
  • --test_data_pattern ../tf-data/handwritten-test-{}.tfrecords
  • --max_steps 6000
  • --batch_size 20
  • --beam_size 3
  • --input_chanels 1
  • --model: the class from handwritten models.py
  • --base_learning_rate 0.001
  • --num_readers 2
  • --export_model_steps 500
  • --display_step 10
  • --display_step_lme 100
  • --start_new_model
  • --hidden: lstm number of neurons
  • --layers: number of layers of lstm cell

In order to see statistics in tensorboard:

tensorboard --logdir=separable_lstm --port=8080
label rate error for test images

ctc loss for test images

Inference