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Single Character Recognition

Single Character Recognition. CS 5185-MULTIMEDIA TECHNOLOGIES AND APPLICATIONS 2007 1st Semester - Group Project Progress Presentation Presented on 2007-10-23. Project Group. Group 08 Members: Ku Heung Chin (Ku) Yu K a m Fung (Kam) Fung K a Hang (Harry) W ang Yang       .

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Single Character Recognition

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  1. Single Character Recognition CS5185-MULTIMEDIA TECHNOLOGIES AND APPLICATIONS 2007 1st Semester - Group Project Progress Presentation Presented on 2007-10-23

  2. Project Group • Group 08 • Members: • Ku Heung Chin (Ku) • Yu Kam Fung (Kam) • Fung Ka Hang (Harry) • Wang Yang       

  3. Usage of Content Recognition • Printed content • Business Card Recognition • Car License plate recognition • Handwritten content • Postal address recognition • Bank cheque signature verification

  4. A top-down approach Single Character Recognition is the basis for Content Recognition !!

  5. Implementation • Programming Language • Java (J2SE 1.6) • Platform independent • Rich graphics and image processing APIs • Vector data structure APIs • Development platform • Eclipse - Java SDK • User friendly interface • Java complier and debugger • Java Syntax and spelling checking

  6. Character DB Preprocessing A … G … G Recognition How does it work?

  7. Preprocessing • Image to *.bmp format • Color(RGB) image to Grayscale image • Noise Filter • Binarization • Slant Correction • Thinning • Size Normalization

  8. Image to *.bmp format • FileChooser Class to select input image. • Accepted different image format, e.g. jpg, gif, etc… • Call some API to convert to 24-bit RGB Bitmap format (will be implemented in later phase)

  9. RGB to 8-bits Grayscale 0.3*Red + 0.59*Green + 0.11*Blue

  10. Median:80 Noise Filter • 3x3 pixels Median Filter

  11. Binarization Purpose : • Clear cut the background and the character

  12. Binarization Implementation (Simplest Approach) : • Assumption that noises are filtered out and dark text and white background • Transform gray color to black (0x00) and white (0xFF)

  13. Binarization Implementation (Simplest Approach) : • Average the color as a cut off 0xFF 0x00

  14. Slant Difficulty : • Different slant style @ ppl Solution : • Estimate the slant by a slope • Skew the image back

  15. Slant Correction (Linear Regression)

  16. Thinning Purpose : • Transform a binary image into one pixel thickness

  17. Thinning Implementation (Simple Approach) : • http://fourier.eng.hmc.edu/e161/lectures/morphology/node2.html

  18. Thinning • Will add more on implementation

  19. Image Scaling • Image scaling is a process of resizing an image. Mapping the pixels from the original image to the destination image. • It’s non-trivial process that involves a trade-off between efficiency, smoothness and sharpness.

  20. Image Scaling • Scaling method used in this prototype 1st: Edge Detection: 2nd: Calculate scaler. 3rd: Do scaling: Convert scaler into integer, enlarge every pixel by scaler times. i.e. Scaler = 2.

  21. Image Scaling • Pros: Easy to implement. • Cons: Suitable for integer scaler, not so good for rational scaler.

  22. Recognition • Usually machine learning algorithms • Simple pixel-by-pixel difference would be used as not closely related to this course. • Will be implemented in later phase.

  23. Gantt Chart

  24. Program Live Demo

  25. Q &A

  26. The End Thank you for you attentions!!

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