Radial Sector Coding at SCIS & ISIS 08

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This is the presentation on Translation, Rotation, and Scaling Invariant Character Recognition at SCIS & ISIS 08, Japan

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Radial Sector Coding at SCIS & ISIS 08

  1. 1. A Novel Algorithm for Translation, Rotation and Scale Invariant Character Recognition Asif Iqbal, A. B. M. Musa, Anindya Tahsin, Md. Abdus Sattar, Md. Monirul Islam, and K. Murase SCIS & ISIS 08
  2. 2. Overview <ul><li>Introduction </li></ul><ul><li>Advantages over existing methods </li></ul><ul><li>Radial Sector Coding </li></ul><ul><ul><li>Center of Mass </li></ul></ul><ul><ul><li>Axis of Reference </li></ul></ul><ul><ul><li>Line of Reference </li></ul></ul><ul><ul><li>Feature vector generation </li></ul></ul><ul><li>Classifier </li></ul><ul><li>Experimental Results </li></ul><ul><li>Analysis </li></ul><ul><li>Conclusion </li></ul>
  3. 3. Introduction <ul><li>Invariant Character Recognition (ICR) is recognition of characters independent of translation, rotation and scaling </li></ul><ul><li>It is still a hard problem in computer vision </li></ul><ul><li>Most of the existing algorithms are computationally too expensive or cannot perform well under all three transformations </li></ul><ul><li>Here we propose a simple and inexpensive algorithm for ICR which performs well under all three transformations </li></ul>
  4. 4. Advantages over existing methods Our Radial Sector Coding (RSC) is simple and inexpensive <ul><li>Moment Based Methods: </li></ul><ul><ul><li>Invariant moments are used </li></ul></ul><ul><ul><li>Computationally too expensive </li></ul></ul><ul><ul><li>Examples: Cartesian moment, Zernike moment, Pseudo-Zernike, Orthogonal Fourier-Mellin moments </li></ul></ul>
  5. 5. Advantages over existing methods RSC do not sample whole character for feature extraction <ul><li>Projection Methods: </li></ul><ul><ul><li>Projection is taken for whole character </li></ul></ul><ul><ul><li>Data redundancy exists </li></ul></ul>
  6. 6. Advantages over existing methods (contd.) RSC consider whole area of character for feature extraction <ul><li>Boundary Methods: </li></ul><ul><ul><li>Sample boundary of character only </li></ul></ul><ul><ul><li>Good for solid object recognition </li></ul></ul><ul><ul><li>Not good for character recognition as they have much topological information inside </li></ul></ul>
  7. 7. Advantages over existing methods (contd.) RSC uses only single circle and its radii for feature extraction <ul><li>Radial Coding and SAFER: </li></ul><ul><ul><li>Use multiple concentric circles </li></ul></ul><ul><ul><li>Small circles create erroneous features </li></ul></ul>
  8. 8. Radial Sector Coding
  9. 9. Center of Mass A CoM locates the character independent of location in the Image Center of Mass (CoM) If is the CoM then Here is the Cartesian Moment of order (p+q)
  10. 10. Axis of Reference (Symmetric Characters) A Axis of Reference is the Axis of Symmetry of symmetric characters Enclosing Circle Radii deviding the circle into n sectors Cutpoints Cutpoints with maximum distance Is it Axis of Reference (AoR) ? Not Equal Almost Equal ! Not Equal It is not AoR as pair wise max cutpoint distances are not equal in most of the cases Is it Axis of Reference (AoR) ? Almost Equal ! It is AoR as pair wise max cutpoint distances are equal in most of the cases Hence the name Radial Sector Coding Cutpoint is the pixel of intensity change
  11. 11. Axis of Reference (Symmetric Characters) [contd.] <ul><li>For Symmetric characters the summation of absolute difference of maximum cutpoints distances for each pair of lines having same angular distance from Axis of Symmetry/Axis of Reference will be very small </li></ul><ul><li>We can exploit this fact to find AoR/AoS </li></ul><ul><li>As we do not know the actual AoR/AoS we can consider each axis as a potential AoR/AoS and the one having minimum summation is the actual AoR/AoS </li></ul>
  12. 12. Axis of Reference (Symmetric Characters) [contd.] Let denotes maximum cut-point distance along each radius and initial sampling starting at 0 degree is Now there exists an ordering where is minimum with respect to all other orderings Now let the points for and are and The line connecting and is the AoR
  13. 13. Axis of Reference (Non-symmetric Characters) <ul><li>The Axis found with the minimum summation criteria is a rotation invariant feature for non-symmetric characters also </li></ul><ul><li>So with minimum sum criteria we are getting Axis of Reference which is the Axis of Symmetry for symmetric characters and a rotation invariant feature for all characters </li></ul>
  14. 14. Axis of Reference Examples AoR of Symmetric Character A AoR of Non-symmetric Character F
  15. 15. Line of Reference <ul><li>Line of Reference is one of the two radii on Axis of Reference which has the largest cutpoint distance compared to other one </li></ul>If is the CoM and , are end points of AoR then line connecting , Is the LoR if it has greater cutpoint distance
  16. 16. Line of Reference Examples LoR of Symmetric Character A LoR of Non-symmetric Character F
  17. 17. Feature vector generation <ul><li>Feature vector size 18 is used </li></ul><ul><li>Line of Reference is considered as 0 ° line </li></ul><ul><li>Average distances of cutpoints on 18 radii is calculated starting with LoR </li></ul><ul><li>Feature vector consists of these 18 values </li></ul>
  18. 18. Radial Sector Coding in Brief <ul><li>Step 1: Find Center of Mass (CoM) </li></ul><ul><li>Step 2: Find radius r of enclosing circle </li></ul><ul><li>Step 3: Draw n radii at equal angular distance to divide the circle into n sectors </li></ul><ul><li>Step 4: Find cutpoints on each radius </li></ul><ul><li>Step 5: Calculate maximum and average cutpoint distances </li></ul><ul><li>Step 6: Find Axis of Reference (AoR) </li></ul><ul><li>Step 7: Fine Line of Reference (LoR) </li></ul><ul><li>Step 8: Consider LoR as 0 ° line and generate feature vector of size n/2 </li></ul>
  19. 19. Classifier <ul><li>Multilayer feed-forward ANN is used as classifier </li></ul><ul><li>ANN has good noise tolerance </li></ul><ul><li>ANN has good generalization ability </li></ul>
  20. 20. Experimental Results <ul><li>Experimental Setup </li></ul><ul><ul><li>Matlab is used for feature generation and experimental evolution </li></ul></ul><ul><ul><li>Three layer feed-forward ANN is used for experimentation </li></ul></ul><ul><ul><li>Two widely used fonts Arial and Tahoma is used </li></ul></ul><ul><ul><li>Large sample of 26 uppercase English characters from both fonts are used </li></ul></ul>
  21. 21. Experimental Results (contd.) Recognition rate for Arial font. 40x40 pixel 0° to 90° rotated characters at 10° gap are used for training. 40x40 pixel 0° to 350° rotated characters at 10° gap are used for testing. Total number of training characters is 26x10 = 260 . Total number of test characters is 26x36 = 936 Character Accuracy Character Accuracy A 100 N 100 B 100 O 100 C 100 P 100 D 100 Q 100 E 100 R 100 F 94.44444 S 100 G 91.66667 T 100 H 100 U 100 I 100 V 100 J 100 W 100 K 97.22222 X 100 L 100 Y 100 M 100 Z 100 Average 99.35897
  22. 22. Experimental Results (contd.) Recognition rate for Arial font. 40x40 pixel 0° to 135° rotated characters at 15° gap are used for training. 40x40 pixel 0° to 355° rotated characters at 5° gap are used for testing. Total number of training characters is 26x10 = 260 . Total number of test characters is 26x72 = 1872 Character Accuracy Character Accuracy A 98.611111 N 100 B 100 O 100 C 100 P 100 D 100 Q 97.222222 E 100 R 100 F 88.888889 S 100 G 98.611111 T 100 H 100 U 98.611111 I 100 V 100 J 100 W 97.222222 K 94.444444 X 97.222222 L 100 Y 97.222222 M 100 Z 100 Average 98.77137
  23. 23. Experimental Results (contd.) Recognition rate for Arial font. 50x50 pixel 0° to 90° rotated characters at 10° gap are used for training. 50x50 pixel 0° to 350° rotated characters at 10° gap are used for testing. Total number of training characters is 26x10 = 260 . Total number of test characters is 26x36 = 936 Character Accuracy Character Accuracy A 100 N 100 B 91.666667 O 100 C 100 P 100 D 100 Q 97.222222 E 100 R 100 F 97.222222 S 97.222222 G 100 T 100 H 100 U 100 I 100 V 100 J 100 W 100 K 100 X 100 L 100 Y 100 M 100 Z 83.333333 Average 98.71795
  24. 24. Experimental Results (contd.) Recognition rate for Arial font. 30x30 pixel 0° to 90° rotated characters at 10° gap are used for training. 30x30 pixel 0° to 350° rotated characters at 10° gap are used for testing. Total number of training characters is 26x10 = 260 . Total number of test characters is 26x36 = 936 Character Accuracy Character Accuracy A 100 N 100 B 100 O 100 C 94.444444 P 100 D 100 Q 97.222222 E 100 R 100 F 100 S 83.333333 G 88.888889 T 100 H 97.222222 U 100 I 97.222222 V 100 J 100 W 100 K 97.222222 X 97.222222 L 94.444444 Y 100 M 100 Z 100 Average 97.97009
  25. 25. Experimental Results (contd.) Recognition rate for Arial font. 40x40 pixel 0° to 90° rotated characters at 10° gap are used for training. 50x50 pixel 0° to 350° rotated characters at 10° gap are used for testing. Total number of training characters is 26x10 = 260 . Total number of test characters is 26x36 = 936 Character Accuracy Character Accuracy A 100 N 100 B 100 O 100 C 100 P 100 D 100 Q 100 E 100 R 100 F 100 S 100 G 100 T 100 H 97.222222 U 100 I 97.222222 V 100 J 100 W 100 K 100 X 100 L 100 Y 100 M 100 Z 100 Average 99.78632
  26. 26. Experimental Results (contd.) Recognition rate for Tahoma font. 40x40 pixel 0° to 90° rotated characters at 10° gap are used for training. 50x50 pixel 0° to 350° rotated characters at 10° gap are used for testing. Total number of training characters is 26x10 = 260 . Total number of test characters is 26x36 = 936 Character Accuracy Character Accuracy A 100 N 100 B 55.555556 O 100 C 100 P 100 D 100 Q 100 E 100 R 91.666667 F 91.666667 S 100 G 100 T 100 H 100 U 97.222222 I 100 V 100 J 100 W 88.888889 K 100 X 97.222222 L 100 Y 100 M 100 Z 100 Average 97.00855
  27. 27. Experimental Results (contd.) Average recognition rate for all characters considering previous tables Character Accuracy Character Accuracy A 99.7685185 N 100 B 91.20370383 O 100 C 99.074074 P 100 D 100 Q 98.611111 E 100 R 98.61111117 F 95.37037033 S 96.75925917 G 96.52777783 T 100 H 99.074074 U 99.3055555 I 99.074074 V 100 J 100 W 97.68518517 K 98.148148 X 98.611111 L 99.074074 Y 99.537037 M 100 Z 97.22222217 Average 98.60221
  28. 28. Analysis <ul><li>Correlation of Features </li></ul>RSC generates highly correlated features under different rotation
  29. 29. Analysis (contd.) <ul><li>Discrimination capability for similar </li></ul><ul><li>characters </li></ul>RSC generates enough distinctive features for similar characters
  30. 30. Analysis (contd.) <ul><li>Double Mirror Symmetry </li></ul><ul><ul><li>Characters like H, I, O has double </li></ul></ul><ul><ul><li>Axis of Symmetry </li></ul></ul>Double Mirror Symmetry can be exploited in future
  31. 31. Analysis (contd.) <ul><li>Double Reverse Mirror Symmetry </li></ul><ul><ul><li>Characters like N, S, Z are symmetric if we reverse </li></ul></ul><ul><ul><li>the mirror reflected part </li></ul></ul>Double Reverse Mirror Symmetry can be exploited in future Horizontal Reverse Mirror Symmetry Vertical Reverse Mirror Symmetry
  32. 32. Analysis (contd.) <ul><li>Inherent Difficulties </li></ul><ul><ul><li>Finite Resolution </li></ul></ul><ul><ul><ul><li>Sampling is limited by finite resolution of image </li></ul></ul></ul><ul><ul><li>Round Up Error </li></ul></ul><ul><ul><ul><li>Any measure required to be mapped to image requires rounding up </li></ul></ul></ul><ul><ul><li>Boundary Distortion </li></ul></ul><ul><ul><ul><li>Rotation introduces unavoidable boundary distortion </li></ul></ul></ul>
  33. 33. Conclusion <ul><li>RSC is simple and inexpensive </li></ul><ul><li>Experimental results prove its effectiveness </li></ul><ul><li>Use of more sophisticated classifier in future may improve its performance </li></ul><ul><li>Double mirror and reverse mirror symmetry can be exploited in future </li></ul>
  34. 34. Thanks !

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