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OCR using PCA. Ohad klausner. Introduction. What is OCR?? Optical character recognition. What is PCA???? principal components analysis reducing dimnesionalty in a dataset retaining characteristics of the dataset. Why PCA?. Appearance based recognition Suited for OCR

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ocr using pca

OCR using PCA

Ohad klausner

introduction
Introduction
  • What is OCR??
    • Optical character recognition
  • What is PCA????
    • principal components analysis
      • reducing dimnesionalty in a dataset
      • retaining characteristics of the dataset
why pca
Why PCA?
  • Appearance based recognition
    • Suited for OCR
  • optimal linear transformation subspace
    • Less compution time
    • Minimal lose of accuracy
why not problems
Why not? (problems….)
  • Poor recognition rate: 50%
  • Two main problems:
    • Location sensitivity:
      • image: recognized as:
  • Similar letters:
    • image: recognized as:

Kaph Sofit Resh

ocr enhancements
OCR enhancements
  • Choosing k
    • Accuracy and compution time tradeoff
  • Creating a whole word OCR
    • minimizes the similar letters mistake
  • Centering the letters
    • Solves location sensitivity problem