A seminar of Ph.D. theses which explain a proposed system for recognize the Arabic handwritten text and identify the text writer. Several proposed steps are described in details in this seminar and the obtained results are viewed in detail.

1. Arabic Handwritten Text Recognition and Writer
Identification
Supervisor:
Asst. Prof . Dr. Alia K. Abdul Hassan
Prepared by:
Mustafa Salam Kadhm
2017
Ministry of Higher Education &
Scientific Research
University of Technology
Department of Computer Science

2. Contents
Problem Statement1
Aim of Thesis2
Proposed System3
Experiments and Results4
Conclusions5

3. Problem Statement
3
• Most of the governments and organizations have handwritten need to be editable and searchable.
• Arabic handwritten text recognition is a complex process compared with other handwritten languages
because it is cursive in nature.
• Poor obtained accuracy of existing recognition systems (depended on character segmentation).
• Unauthenticated recognition results of the existing systems.
• The availability problem of Arabic handwritten database.

4. Aim of Thesis
4
 Develop an accurate Arabic handwritten text recognition system based on multi-scale features
extraction methods and SVM classifier.
 Employ the proposed system in a security application by identifying the writer of the input
handwritten text.
 Develop an Arabic handwritten database with colored and gray handwritten images that works
for character, word, text recognition system and can be used for the security applications.

5. The Proposed System Architecture
5
Input Output

6. The Proposed System Architecture
6
Input
Output

7. Image Acquisition
7
Gray
Color
Color

8. 8
Segmentation
Input Image
applying dilation
and filling
drawing rectangles around the objectsDrawing the obtained
rectangles on original image
handwritten sub-images
applying Sobel filter

9. Preprocessing (Image Thresholding )
9
AHDB
IESK-arDB
Proposed
Input Image (gray) Output Image (binary)

10. Preprocessing (Noise Removal)
10
Noise Removed
Noise Removed

11. Preprocessing
11
Image 1
Image 2
Edge Detection
Image 3

12. Preprocessing (Image Normalization)
12
Image 1
Image 2
Image 3
128 x 128

13. Features Base1 Construction
13
Image 1
Structural Features

14. Features Base1 Construction
14
Block 1
Block 3
Block 2
Block 4
Image 2
Statistical Features

15. Features Base1 Construction
15
Image 2
Take first 10 coefficients in
zigzag order
DCT Features
Discrete Cosine Transform (DCT) Features

16. Features Base1 Construction
16
Image 3
X-axis
Y-axis
Magnitude Orientation
Modified Histogram of Oriented Gradient (MHOG1) Features

17. Features Base1 Construction
17
Modified Histogram of Oriented Gradient (MHOG1) Features

18. Features Base1 Construction
18
Interpolation votes of gradient orientation
Modified Histogram of Oriented Gradient (MHOG1) Features

19. Features Base1 Construction
19
Feature Vector

20. Classification
20

21. Post-processing
21
Arabic Text Unicode
‫هللا‬ [ 1575,1604,1604,1607]
‫عبد‬ [1593,1576,1583]
‫العام‬ [ 1575,1604,1593,1575,1605]
Proposed Arabic lexicon

22. Features Base2 Construction (module2)
22
Modified Histogram of Oriented Gradient (MHOG2) Features

23. Shape Features
23
Image 4
Shape Features

24. Classification (module2)
24

25. Post-processing (module2)
25
Class Label Writer Name
[1] Writer1
[2] Writer2
.
.
.
.
.
.
[n] Writer(n)
Proposed writers’ lexicon

26. Proposed Arabic Handwritten Database
26

27. Proposed Arabic Handwritten Database
27

28. Proposed Arabic Handwritten Database
28

29. Proposed Arabic Handwritten Database
29

30. Experiments and Results
30
Database Correct Segm. Under Segm. Over Segm. Misplaced Segm.
AHDB 89% 3% 6% 2%
Proposed 92% 4% 2% 2%
 Segmentation
Testing Set = 50
True Positive = 46
Accuracy =
46
50
x 100

31. Evaluation of The AHTRS System (module1)
31
 Preprocessing ( image thresholding)

32. Evaluation of The AHTRS System (module1)
32
 Preprocessing ( noise removal)

33. Evaluation of The AHTRS System (module1)
33
Preprocessing ( noise removal)

34. Evaluation of The AHTRS System (module1)
34
System Accuracy
AHTRS system without BSE algorithm 93%
AHTRS system + BSE algorithm 96.317%
Preprocessing ( black space elimination)
Image Size Accuracy
32 x 32 94 %
64 x 64 94.8%
64 x 128 95.22%
128 x 64 95%
128 x 128 96.317%
Preprocessing ( image normalization)

35. Evaluation of The AHTRS System (module1)
35
Edge Detection Filter Accuracy
HOG filter 89.2%
Sobel 89%
Canny 87%
Roberts 90.1%
Proposed 92.70%
 Features Extraction( MHOG1)
Approach Accuracy
un-overlapped blocks 88.5%
overlapped blocks 92.70%

36. Evaluation of The AHTRS System (module1)
36
Blocks Accuracy
1 block 67.92%
4x4 blocks 60%
6x6 blocks 61.22%
8x8 blocks 64.7%
Ordering technique Accuracy
Sequential 66.7%
Zig-zag 67.92%
Method Extraction Time
DCT 1.6
FCT 0.8
Features Extraction( DCT)

37. Evaluation of The AHTRS System (module1)
37
Features Accuracy
DCT 67.92%
MHOG1 92.70%
Statistical + Structural 70.88%
All features 96.317%
Features Extraction
Features Classification Time
Without FN 4.5
With FN 0.9
 Features Normalization

38. Evaluation of The AHTRS System (module1)
38
 Classification

39. Evaluation of The AHTRS System (module1)
39
Database Kernel Accuracy
AHDB linear 92%
AHDB polynomial 96.317 %
AHDB RBF 93.1%
IESK-arDB linear 76%
IESK-arDB polynomial 82 %
IESK-arDB RBF 78.66%
Proposed linear 96.2%
Proposed polynomial 98%
Proposed RBF 97%
Classification
Testing Set = 1365
True Positive = 1314
Accuracy =
1314
1365
x 100

40. Evaluation of The AHTRS System (module2)
40
Features Accuracy
MHOG2 95.9%
Shape 93 %
MHOG2 + Shape 100%
 Features Extraction ( module2)

41. Evaluation of The AHTRS System (module2)
41
Approach Kernel Accuracy
Sub-word level linear 80%
Sub-word level polynomial 85 %
Sub-word level RBF 81.9%
Text level linear 98%
Text level polynomial 100%
Text level RBF 98.6%
 Classification ( module2)

42. Evaluation of The AHTRS System
42
Module Classifier Accuracy
1 KNN 93%
1 SVM 98%
1 ANN 94%
2 KNN 95%
2 SVM 100%
2 ANN 98%
Classification ( module 1 & 2)

43. Conclusions
1. The proposed system depends on handwritten sub-images segmentation approach which
is simple, practical and efficient and leads to more accurate accuracy than of the systems that
depends on the character segmentation.
2. The steps of the proposed preprocessing stage lead to efficient results of binary, thinned and
cropped images without noise that increase the system accuracy. Besides, the choose of
appropriate edge detector and image normalization size enhance the obtained outcomes of
the system. information of the handwritten text.
43

44. Conclusions Cont.
3. The employment of MHOG1 and MHOG2 in the proposed system is the main successful part of
this thesis which leads to better recognition and identification accuracy. Furthermore, the
obtained results show the strength of using the proposed edge detection filter for HMOG1 over
the other filters.
4. The proposed features, DCT, statistical and shape features in another hand, are made the
system more accurate.
5. The training and classification time are reduced by features normalization (FN) algorithm,
subsequently reducing the system processing time.
44

45. Conclusions Cont.
6. The use of one vs all approach with polynomial kernel of Support Vector Machines (SVM)
classification algorithm yields more robust recognition results and identification performance than
the use of other approaches, kernels and classifiers.
7. The proposed system has achieved better accuracy with three different Arabic handwritten
databases than all the previous works.
8. The proposed text handwritten database gives a better accuracy result than the other handwritten
databases, and it can works in identification. Besides, the database can work for character and
word recognition
45

46. List of Publications
Journals:
1. Mustafa S., Alia K., ”ACRS: Arabic Character Recognition System Based on Multi Features Extraction Methods”, International Journal of Scientific and Engineering
Research, vol. 6, Issue 10, pp. 656-661, 2015.
2. Alia K., Mustafa S., “Handwriting Word Recognition Based on SVM Classifier”, International Journal of Advanced Computer Science & Applications, vol. 1, issue 6, pp. 64-
68, 2015.
3. Mustafa S., Alia K., “Handwriting Word Recognition Based on Neural Networks” International Journal of Applied Engineering Research, vol. 10, issue 22, pp. 43120-
43124, 2015.
4. Alia K., Mustafa S., “An Efficient Image Thresholding Method for Arabic Handwriting Recognition System”, Engineering and Technology Journal, vol. 34, issue 1, pp.
26-34, 2016.
5. Alia K., Mustafa S., “An Efficient Preprocessing Framework for Arabic Handwriting Recognition System”, Diyala Journal For Pure Sciences, vol. 12, issue 3, pp. 147-
163, 2016.
6. Alia K., Mustafa S., “Arabic Handwriting Text Recognition Based on Efficient Segmentation, DCT and HOG Features”, International Journal of Multimedia and
Ubiquitous Engineering, vol. 11, issue 10, pp. 83-92, 2016.
Conferences:
1. Alia K., Mustafa S., “AHCR: Arabic Handwriting Character Recognition System Using Multi-scale Features, SVM And KNN Classifiers”, 2nd Global Conference on
Contemporary Issues in Education, 2nd Global Conference on Contemporary Issues in Education, pp. 46, 2015.
2. Alia K., Mustafa S., “Arabic Handwriting Text Recognition Based on EOD and HOG Features”, SAI Intelligent Systems Conference (IntelliSys), 2016. (Accepted)
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47. Thank you