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OCR

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OCR OCR Presentation Transcript

  • OCR Algorithms
    • Jacek Bajor
  • OCR
    • Optical Character Recognition
    • Translation of printed or handwritten text to digital form
    KAZ 813
  • OCR
    • Fields
      • Pattern Recognition
      • Artificial Intelligence
      • Machine Learning
      • Computer Vision
  • Applications
    • Digitalising libraries
  • Applications
    • books.google.com
  • Applications
    • Office work
  • Applications
    • Captchas
    Mr. blocked
  • Applications
    • Recognising licence plates
    • And many, many others...
  • OCR Algorithms
    • Artificial Neural Networks
  • Why ANN?
  • Why ANN?
    • They are flexible
    • They can be thought
    • They can learn themselves
    • They are powerful
  • ANNs
    • Let’s assume ANN is a black magical box
  • ANNs
    • Learning stage
    input output a a a a a a
  • ANNs
    • Evaluation
    98% a 5% a?
  • ANN structure Neuron (nerve cell)
  • ANN structure
  • ANN structure
  • ANNs types
    • Feedforward
    • Recurrent (back-propagation)
    • Radial basis function network
    • Kohonen self-organizing network
    • Others...
  • ANNs applications
    • Optical Character Recognition
    • Function approximation
    • Artificial Intelligence
    • Finance
    • Speech analysis
    • Many, many others
  • Advantages
    • Flexible
    • Efficient if well designed
  • Disadvantages
    • Focused on one activity
    • Give approximations
    • Inefficient if poorly designed
  • Libraries
    • FANN - Fast Artificial Neural Network Library - leenissen.dk/fann
    • Annie - annie.sourceforge.net
    • Libann - savannah.nongnu.org/projects/libann
  • Thank you