/Writer-Identification-3approach

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Writer identification from handwriting using 3 approaches

In this project I used the 'AND' dataset which is labelled with the writer number who wrote that 'and' , The dataset is avalaible in image form of handwritten word 'and' and also in human-engineered feature form (curvature of 'a' , slant of 'd' etc). We use 3 approaches to solve the problem of identifying the writer from the handwritting

  • PGM ( Mulitnet ) We use the human engineered features (curvature of 'a' , slant of 'd' etc) to build our two Bayesian Networks where one would represent the two features are from the same writer and the other would represent they are from diffrent writer. Using this knowledge of probability obtained from Bayesian Networks we can find out whether the two features belong to handwriting of the same writer or not.
  • Siamese Network with extracted SIFT features I extracted SIFT features and from uniform sized features using bag-of-features method and trained it usind a simple neural network.

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Built With

  • Dropwizard - The web framework used
  • Maven - Dependency Management
  • ROME - Used to generate RSS Feeds

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

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This project is licensed under the MIT License - see the LICENSE.md file for details

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