1) The document presents a methodology for offline handwritten Thai character recognition using local binary pattern (LBP) features and shape matrices. 2) An experiment was conducted using 4,400 handwritten Thai character images from 44 classes to test different LBP types and shape matrix sizes. 3) The results found that uniform rotation invariant LBP achieved the highest accuracy of 68.96% and a shape matrix size of 12x12 bits performed best.

1. Offline Handwritten Thai Character
Recognition Using Single Tier Classifier
and Local Features
Ferdin Joe John Joseph1
Panatchakorn Anantaprayoon2
2Kamnoetvidya Science Academy, Rayong
1Faculty of Information Technology, Thai – Nichi Institute of Technology, Bangkok

2. Introduction
Character Recognition
conversion of typewritten or handwritten text into machine-encoded
one.
http://www.cbnco.com/idsys/id-readers-eagle.php
http://cdn.iphonehacks.com/wp-
content/uploads/2013/01/mzl.bmcrwhfl.320x480-75.jpg
1

3. Types of Character Recognition
Offline Character Recognition
https://cs.stanford.edu/people/adityaj/HandwritingRecognition.pdf
Online Character Recognition
https://nl.pcmag.com/google-1/1723/news/handschrift-app-van-google-nu-beschikbaar

4. Introduction
2
Preprocessing
Input image
Feature Extraction
Collect data features
Classifier
Start
End
Classification
Class
Preprocessing
Input image
Feature Extraction
Classifier and
Validation
Start
End
Y
N
TRAINING TESTING
Character Recognition

5. Objective
To develop handwritten Thai alphabets recognition methods
using Local Binary Pattern (LBP)
3

6. Methodology
I. Experiment Design
II. Datasets Preparation
III. Coding and Testing
4

7. I. Experiment Design: Overall
5
1) Grayscaling,
2) 2D median filter
3) Binarization
4) Complementation
5) Edge detection
6.1) Cropping for LBP calculation, 6.2) Cropping for shape matrices calculation
LBP Shape Matrix
Classification
Class
Preprocessing
Input image
Feature Extraction
All images?
Start
End

8. Preprocessing
6
1) Grayscaling, 2) 2D median filter, 3) Binarization, 4) Complementation, 5) Edge detection, 6.1)
Cropping for LBP calculation, 6.2) Cropping for shape matrices calculation

9. Local Binary Pattern: LBP
7
LBP feature

10. Local Binary Pattern: LBP
8
1 image, 1 histogram!
Histogram of LBP features

11. LBP
20

12. Rotation Invariant LBP
21
1113
3
19
4
9717
6
18
8
4422
LBP
feature

13. The 58 different uniform
patterns in (8,R) neighborhood
http://www.codeproject.com/KB/graphics/741559/lbp5.png
Uniform LBP
22

14. Uniform LBP
23
https://i.stack.imgur.com/tH5jp.png

15. Uniform Rotation Invariant LBP
24
Unifor
m
Non-
uniform
(LBP
code=9)

16. Shape Matrix
9
0000001110011100101000000
Shape feature

17. Visual Program
10

18. I. Experiment Design
11
Experiment 2: Additional conditions that help to increase accuracy
Variables
• Size of shape matrix–25, 121, 144, 169, 196, 225 bits
Experiment 1: LBP types that yield the highest accuracy
Variables
• LBP types–Normal, Rotation invariant, Uniform, Uniform rotation invariant
MAIN EXPERIMENT

19. 12
44 Thai alphabets/form
Total: 5,148 letters
(117 forms)
Used: 4,400 letters
II. Datasets
Preparation

20. II. Datasets Preparation
13
Naming: XX-XXX.jpg
Class
(00,01,…,44) Sample No.
(001,002,…)

21. III. Coding/Testing
14

22. Query Matching
1 1 0 1
0 0 0 0
1 1 1 1
Tested feature
Sample features
1 1 0 1
0 0 1 0
1
2
p1 p2 p3 p4
q1 q2 q3 q4
q1 q2 q3 q4
(𝒑𝒑𝒊𝒊 − 𝒒𝒒𝒊𝒊)𝟐𝟐
:
(𝒑𝒑𝒊𝒊 − 𝒒𝒒𝒊𝒊)𝟐𝟐
:
25

23. Query Matching
0 0 0 0
1 1 1 1
Sample features
1 1 0 1
0 0 1 0
3
1
Euclidean distance
1
2
26

24. Results & Conclusion
 Experiment 1: LBP types
 Experiment 2: Additional conditions
15

25. Experiment 1: LBP types
16
Number Type of LBP / bins Accuracy (%)
1 Normal LBP/ 64 68.05
2 Rotation invariant LBP/ 36 68.89
3 Uniform LBP / 59 68.96
4 Uniform rotation invariant LBP/ 10 67.42

26. Comparison with other Methods
S.No Methodology Classification Accuracy Classification basis of characters
1 [3] Ant Miner algorithm 82.7% Grouping
2 [2] Genetic Algorithm NA Grouping
3 [4] Single stage classifier with global features alone 54.61% Grouping
4 [4] Single stage classifier with global and local features 78.89% Grouping
5 Proposed Methodology using 64 bin LBP 68.82% Individual
6 Proposed Methodology using 59 bin LBP 68.96% Individual

27. Advantages over Existing Methodologies Listed
Paper: C. Pornpanomchai, V. Wongsawangtham, S.
Jeungudomporn, and N. Chatsumpun, “Thai Handwritten
Character Recognition by Genetic Algorithm (THCRGA),” Int. J.
Eng. Technol., vol. 3, no. 2, pp. 148–153, 2011.
• Ours is offline character recognition
• Dataset used is not available
• Genetic Algorithm is slower for 44 classes
• Feature set indices are not mentioned

28. Advantages over Existing Methodologies Listed
• Paper: P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S.
Phaiboon, and C. Kimpan, “Off-Line Hand Written Thai Character
Recognition using Ant-Miner Algorithm,” Int. J. Comput. Electr.
Autom. Control Inf. Eng., vol. 8, no. 1, pp. 276–281, 2005.
• Dataset used is not available currently
• Ant Miner was slow when we tried with some matlab code on our dataset
• Grouped only to 5 classes but our proposed methodology used 44 classes
• Offline but the sampling methodology is not available

29. Advantages over Existing Methodologies Listed
• Paper: I. Methasate, S. Marukatat, S. Sae-Tang, and T.
Theeramunkong, “The feature combination technique for off-line Thai
character recognition system,” in Proceedings of the International
Conference on Document Analysis and Recognition, ICDAR, 2005, vol.
2005, pp. 1006–1009..
• Methodology seems impressive but classifying criteria is not same as
our proposed methodology
• Grouped only to 20 classes but our proposed methodology used 44
classes
• Similarly looking characters false negative is taken as correct
classification

30. Experiment 2: Additional conditions
17
Variable
Size of shape matrix–5x5, 11x11, 12x12, 13x13, 14x14, 15x15 bits
Results
12x12 > 11x11 > 13x13 > 14x14 > 15x15 > 5x5 bits

31. Future work
• Train with more datasets and include vowels and intonation
• Try other classifiers e.g. SVM, Neural Network and Deep
Learning
18

32. References
[1] “Thai Language,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Thai_language.
[Accessed: 04-Nov-2016].
[2] C. Pornpanomchai, V. Wongsawangtham, S. Jeungudomporn, and N. Chatsumpun, “Thai
Handwritten Character Recognition by Genetic Algorithm (THCRGA),” Int. J. Eng. Technol., vol. 3, no.
2, pp. 148–153, 2011.
[3] P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, and C. Kimpan, “Off-Line
Hand Written Thai Character Recognition using Ant-Miner Algorithm,” Int. J. Comput. Electr. Autom.
Control Inf. Eng., vol. 8, no. 1, pp. 276–281, 2005.
[4] I. Methasate, S. Marukatat, S. Sae-Tang, and T. Theeramunkong, “The feature combination
technique for off-line Thai character recognition system,” in Proceedings of the International
Conference on Document Analysis and Recognition, ICDAR, 2005, vol. 2005, pp. 1006–1009.
[5] F. J. John Joseph and S. Auwatanamongkol, “A crowding multi-objective genetic algorithm for
image parsing,” Neural Comput. Appl., vol. 27, no. 8, pp. 2217–2227, 2016.
[6] T. Ojala, M. Pietikainen, and D. Harwood, “A Comparative Study of Texture Measures with
Classification Based on Feature Distributions,” Pattern Recognit., vol. 29, no. 1, pp. 51–59, 1996.
[7] P. Anantaprayoon, F. J. John Joseph, and S. Marukatat, “Local Feature Based Offline
Handwritten Thai Character Recognition,” in Proceedings of Thai Japan Student ICT Fair, 2016, p. 41. 19