1. 1 An Efficient Classification Approach Based on Grid Code Transformation and Mask-Matching Method Presenter: Yo-Ping Huang Tatung University

2. 2 Outline 1. Introduction 2. The proposed classification approach 3. The coarse classification scheme 4. The fine classification scheme 5. Experimental results 6. Conclusion

3. 3 1. Introduction Paper documents -> Computer codes OCR(Optical Character Recognition) The design of classification systems consists of two subproblems: Feature extraction Classification

4. 4 Feature extraction Features are functions of the measurements that enable a class to be distinguished from other classes. It has not found a general solution in most applications. Our purpose is to design a general classification scheme, which is less dependent on domain-specific knowledge.

5. 5 Discrete Cosine Transform (DCT) It helps separate an image into parts of differing importance with respect to the image's visual quality. Due to the energy compacting property of DCT, much of the signal energy has a tendency to lie at low frequencies.

6. 6 Two stages of classification Coarse classification DCT Grid code transformation (GCT) Fine classification Statistical mask-matching

7. 7 Figure 1. The framework of our classification approach. Prepro- cessing Feature Extraction via DCT Quanti- zation Grid Code Transfor- mation Sorting Codes training pattern Prepro- cessing Feature Extraction via DCT Searching Candidates test pattern Training Coarse Classification Elimination of Duplicated Codes candidates Quanti- zation Grid Code Transfor- mation Calculate Mask Probability Statistical Mask Matching final decision Fine Classification

8. 8 In the training mode: GCT Positive mask Negative mask Mask probability In the classification mode: GCT (coarse classification) Statistical mask matching (fine classification)

9. 9 Grid code transformation (GCT) Quantization The 2-D DCT coefficient F(u,v) is quantized to F’(u,v) according to the following equation: The most D significant of image O i are quantized and transformed to a code, called grid code (GC), which is in form of [q i1, q i2,.., q iD ].

10. 10 Grid code sorting and elimination The list has to be sorted ascendingly according to the GCs. Redundancy might occur as the training samples belonging to the same class have the same GC. In the test phase, on classifying a test sample, a reduced set of candidate classes can be retrieved from the lookup table according to the GC of the test sample.

11. 11 4. The fine classification scheme Mask Generation A kind of the template matching method The border bits are unreliable Find out those bits that are reliably black (or white).

12. 12 (a) (b) (c) Figure 3. Mask generation (a) Superimposed characters of “ 佛 ”, (b) the positive mask of “ 佛 ”, and (c) the negative mask of “ 佛 ”.

13. 13 Bayes ’ classification P(c i | x) : the probability of x in class i when x is observed. P(x | c i ): the probability of the feature being observed when the class is present. P(c i ) : the probability of that class being present. P(x) : the probability of feature x.

14. 14 Measures for mask matching The degree of matching between an unknown character x and the positive mask of class i,, can be defined by: Similarly, N b ( f ) : the number of black bits in bitmap f. M b (f, g) : the number of black bits with the same positions in both f and g.

15. 15 Def. 1. If x matches to the positive mask of class i at the degree of , i.e., It is called x -match the positive mask of class i, and denoted by. Def. 2. If x matches to the negative mask of class i at the degree of , i.e., It is called x -match the negative mask of class i, and denoted by.

16. 16 Statistical mask-matching The probability of x in class i when is observed can be described by Similarly, we get

17. 17 Statistical decision rule Rule AMP (Average Matching Probability)

18. 18 5. Experimental Results A famous handwritten rare book, Kin-Guan bible ( 金剛經 ) 18,600 samples. 640 classes.

19. 19 Figure 4. Reduction and accuracy rate using our coarse classification scheme. The best value of D is 6.

20. 20 Figure 5. Accuracy rate using both coarse and fine classification. Good reduction rate would not sacrifice the performance of fine classification.

21. 21 Figure 6. Accuracy rate using both coarse and fine classification under different values of AMP.

22. 22 6. Conclusions The experimental results show that: The statistical mask-matching method is effective in recognizing the Chinese handwritten characters. The good reduction rate provided by coarse classification would not sacrifice the performance of fine classification. The more confident the decision, the better the accuracy rate is. By selecting features of strong confidence, classification accuracy could be further improved.