1. Development of an OCR System Nathan Harmata TJHSST Computer Systems Lab 2007-2008

2. What is OCR? Optical Character Recognition Font and handwriting based

3. Goals of My Project Generic recognition for Latin-based fonts Proper handling of most formatting System built from scratch

4. Overview of Idocrase System

5. Image Processing

6. Transformations Attribute Character Model

7. Transformations Sector Vector - image is parsed into parts that pass the vertical line test - then each part is transformed into a collection of line segments Gap Vector - gaps, if any, are found on the four sides of the image

8. Transformations Pixel Concentration Vector – which sides, if any, have a higher concentration of pixels

9. Character Recognition GCDD – Generic Character Definition Database Averages of Character Models for every character from many different fonts 0 PixelConcentrationVector balanced balanced SectorVector 4 3 GapVector

10. Character Recognition For a single character: For words, dictionary and grammar references are used.

11. Idocrase Application

12. Results -Mediocre word recognition -Doesn’t handle formatting well -Doesn’t handle small letters well -Fairly accurate single character recognition (93.7%)‏