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OCR using PCA

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

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  1. OCR using PCA Ohad klausner

  2. Introduction • What is OCR?? • Optical character recognition • What is PCA???? • principal components analysis • reducing dimnesionalty in a dataset • retaining characteristics of the dataset

  3. Why PCA? • Appearance based recognition • Suited for OCR • optimal linear transformation subspace • Less compution time • Minimal lose of accuracy

  4. Why not? (problems….) • Poor recognition rate: 50% • Two main problems: • Location sensitivity: • image: recognized as: • Similar letters: • image: recognized as: Kaph Sofit Resh

  5. 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

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