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This code implements the paper by Sebastian Sudholt, Gernot A. Fink, Christened "PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents"

The paper can be found at: https://arxiv.org/pdf/1604.00187.pdf

I shall write a few steps down for you to run this project in your machine. Please note that I shall consider that you have git, pip3, and virtualenv.

Steps:

• Setup a new virtualenv using: virtualenv -p python3 phocnet_keras
• Install some essential packages using:
  • pip3 install numpy
  • pip3 install pandas
  • pip3 install opencv-python
  • pip3 install tensorflow-gpu (or if you do not have a GPU then, pip3 install tensorflow)
  • pip3 install keras
• Now, Clone this repository using git clone https://github.com/pinakinathc/phocnet_keras
• Go to the directory of project: cd phocnet_keras
• Now, untar the dataset present in word & xml folders using:
  • tar -xvf words/words.tgz
  • tar -xvf xml/xml.tgz
• We are now ready to execute the model. Execute: python phoc.py

Please note, if you do not have a GPU in your computer, you should comment the following lines:

• phoc_classifier.py => lines: {13-17}, 19, 75

If you have a GPU but do not have multiple GPUs in your system, please comment like:

• phoc_classifier.py => line: 75

I have not completed training, hence my model has an MAP of only 62% whereas the original paper claims to have map of 72.51%.

Reference

@inproceedings{Sudholt2017-EWS,
   booktitle = {Proc. Int. Conf. on Document Analysis and Recognition},
   author = {Sudholt, Sebastian and Fink, Gernot A.},
   title = {{Evaluating Word String Embeddings and Loss Functions for CNN-based Word Spotting}},
   year = {2017}
}