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Scene Text Detection and Style Classification into Machine Printed and Handwritten Text

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Style Classification into Machine Printed and Handwritten Text

Keras implementation of Faster R-CNN to classify text into Machine Printed and Handwritten Text

Getting Started

Prerequisites

  • numpy
$ pip install numpy
  • h5py
$ pip install h5py
  • opencv-python
$ pip install opencv-python
  • sklearn
$ pip install scikit-learn
  • Keras==2.0.3 (Both theano and tensorflow backends are supported. However tensorflow is recommended)
$ pip install Keras==2.0.3
  • Tensorflow
$ pip install Tensorflow

Trained Model

Trained model can be downloaded from: https://drive.google.com/drive/folders/1eymjiH7_oWJbSI4LOAVhMzzNFtkbYcPz?usp=sharing

How to run Training:

  • Copy pretrained weights for resnet50 (resnet50_weights_tf_dim_ordering_tf_kernels.h5) in Style-Classification directory.

  • train_frcnn.py can be used to train a model. To train the data, it must be in PASCAL VOC format. To train simply do:

$ python train_frcnn.py -p /path/to/train_data/
  • Running train_frcnn.py will write weights to disk to an hdf5 file, as well as all the setting of the training run to a pickle file. These settings can then be loaded by test_frcnn.py for any testing.

How to run Testing:

  • Copy trained model(model_frcnn.hd5) and config.pickle file in Style-Classification diectory.

  • test_frcnn.py can be used to perform inference, given pretrained weights and a config file. Specify a path to the folder containing images:

$ python test_frcnn.py -p /path/to/test_data/

CREDITS

This code is inspired from https://github.com/yhenon/keras-frcnn

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