HandwritingRecognition (MultiDimensional RecurrentNeuralNetworks)

Recognize handwritten text in scanned documents using MultiDimensional Recurrent Neural Networks

Creates a network based on MultiDimensional RNNs architecture using python and cython with Connectionist Temporal Classification (CTC) cost function.

Features

• Creates Multidimensional LSTM network.
• No need to extract features before feeding it to RNN or LSTM framework.
• The current configuration takes 2D input but can be extended to N-dimensional input.
• Uses forward backward algorithm with CTC loss function. This is taken from Andrew Mass stanford-ctc.
• Runs on Multi-Cores.
• Uses cython for fast execution.

Installation

• Installing Cython
• Installing dill. dill extends python’s pickle module for serializing and de-serializing python objects.

Data Preparation

Downloading IAM dataset for handwriting recognition from IAM. To create data splits for training, validation and testing

python create_data.py path_to_xml_files path_to_words train_samples val_samples 

path_to_xml_files: folder where xml files are stored, path_to_words: folder where images of handwritten words are stored, train_samples: no of training samples, val_samples: no of validation samples.

The IAM dataset contains 115149 images of words, so the following command will create 80k training_data 15k validation_data and 20k testing_data.

python create_data.py /home/xml_files/ /home/data/words/ 80000 15000

Training

First create .so file, which will be used for calling cython functions.

python setup_cython_3.py build_ext --inplace

For training, run the following command

python train.py learning_rate momentum regularization update batch_size epochs

For example,

 python train.py --learning_rate=0.001 --momentum=0.9 --reg=0.0 --update=rmsprop --batch_size=200 --epochs=50

Intial weights are initialized using xavier initialization. After every epoch parameters are saved using cPickle as model_parameters.

Testing

Run the following command

python test.py