linecode / handwritten-tf-1.0

Handwritten recognition with slice+cnn+lstm/multidimentional lstm/cnn+lstm

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 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

• In order to see some prediction example checkout the jupyter notebook