1. Statistical Methods for Text Error CorrectionBy
Walid Magdy
Supervision of
Prof. Dr. Mohsen Rashwan
Prof. Dr. Kareem Darwish

2. Outline Motivation Prior Work Contribution Text Fusion
Omni-Font Error Correction
Integrated system
Conclusion
Possible Future Directions
16 November 2018

3. Motivation Massive efforts is moving toward digitization
Digitization is for: Availability & Information Retrieval
OCR is the main enabling technology
OCR systems is far from perfect
Poor quality OCR = Low readability & Low IR effectiveness
Arabic OCR accuracy is much lower than other languages
The need of higher quality text for Arabic documents became a must for improving readability & IR effectiveness.
16 November 2018

4. Prior Work Previous work on OCR focused on:
Building better OCR systems
Improving text quality through text correction
Improving IR effectiveness regardless of improving text quality
Examples:
Sakhr & RDI OCR systems
OCR correction based on character error model and Language model
Index term selection for indexing degraded text
Query garbling based on character error model
16 November 2018

5. Contribution Fusion of multiple OCR output text
Omni-Font OCR error correction
Information retrieval improvement for degraded text
System design that can reduce errors in degraded text by more than 80%
16 November 2018

6. Text Fusion Outlines Definition Approach Implementation
Experimental Setup
Results
16 November 2018

7. Degraded version of text
Text Fusion
Definition
Clean version of text
Noisy edit operations
Degraded version of text
OCR
Sx = S0 + εx
Simage
Previous approaches depends on the presence of only one source of degraded text.
Our approach assumes the presence of more than one version of the degraded text.
Correction
S0’ = S0 + ε0’
Sx = S0 + εx
ε0’ < εx
Fusion
S0’ = S0 + ε0’
S1 = S0 + ε1
S2 = S0 + ε2
Sn = S0 + εn
ε0’ < min(ε1 … εn)
16 November November 2018

8. Text Fusion
Approach
Image
OCR System1
OCR System2
ولا ثدي إلا في ألمستدلاله بنور4 ولا حيا4 إلا في رضا4
ولا ثدي إلا في ألمستدلاله بنور4 ولا حيا4 إلا في رضا4
ولم هدء! إلم في الاستدلال بنوره ولم حياة ملأ في رضا5
ولم هدء! إلم في الاستدلال بنوره ولم حياة ملأ في رضا5
Text Fusion
ولا ثدي إلا في الاستدلال بنوره ولا حياة إلا في رضا5
16 November 2018

9. Best Fitting Word Selection
Text Fusion
Implementation
Word Alignment
Best Fitting Word Selection
Language Model
OCR Text 1
OCR Text 2
Fused Text
OCR1: W1 W2 W3 W4 ….. Wn-2 Wn-1 Wn
OCR2: W1 W2 W3 W4 ….. Wm-2 Wm-1 Wm
16 November 2018

10. Experimental Setup Text Fusion
Fusion was tested in two ways: 1. Effect on Error Reduction 2. Effect on Information Retrieval
ZAD Alma’ad book was used for test, which contains - OCR output using Sakhr - Clean version of the book - Queries with relevance judgments
A tri-gram LM from Ibn-Taymia books
Two OCR systems were available: - Sakhr Automatic Reader - RDI OCR system
16 November 2018

11. Effect on Error Reduction (1/2)
Text Fusion
Effect on Error Reduction (1/2)
Ten clean pages were selected at random & printed in three different fonts (Kufi, Mudir, and Simplified Arabic)
Set contains 4,200 words with 0.9% of words are OOV
Each version of text is scanned with two different resolutions (200x200 dpi & 300x300dpi)
Each scanned version is OCRed using both OCR systems (RDI & Sakhr)
Different versions were fused, & CER’s & WER’s were checked for all version (Original & Fused versions)
16 November 2018

12. Effect on Error Reduction (2/2)
Text Fusion
Effect on Error Reduction (2/2)
200 dpi
300 dpi
RDI
14.6%
4.03%
5.6%
8.73%
4.97%
6.10%
Sakhr
41.8%
34.70%
44.7%
WER for different versions of ZAD test set
Original Version
Fusion Version
Kufi
RDI
25.6%
2.45%
3.0%
3.63%
2.00%
1.91%
Sakhr
8.8%
5.84%
10.7%
Mudir
RDI
56.2%
3.98%
9.4%
11.90%
2.10%
2.50%
Sakhr
16.5%
3.37%
9.1%
Simplified
16 November 2018

13. Effect on Retrieval Effectiveness
Text Fusion
Effect on Retrieval Effectiveness
Relevance judgments on ZAD was built on the whole book (2,730 documents)
To produce multiple versions of degraded text of ZAD:
Sample of the original OCR version were manually corrected
Degraded and Clean text were used to create a character error model based on 1:1 character mapping
Generated model is then used to garble clean version with different CER’s
Different versions were fused, them MAP was used as the figure of merit for IR results
Results showed some improvement in IR effectiveness
16 November 2018

14. Omni-Font Correction Outlines
Idea
Implementation
Experimental Setup
Results
16 November 2018

15. Candidates (Dictionary)
Omni-Font Correction
Idea
Character Error Model
ب ب
ب ي
ب د
ب ق
ب ذ
Domain
Politics
Religious
Science
Sports
Candidates (Dictionary)
الاستقلال
الاستبدال
الاستدلال
الاستبلاد
الاستغلال
الاستبسال
الاستذلال
Religious
الاستبلال
Context
على وجوده
لله تعـالـى
16 November 2018

16. Best Fitting Word Selection
Omni-Font Correction
Implementation
OCR Text
Corrected Text
Generate Candidates
Best Fitting Word Selection
Edit Distance
Language Model
Dictionary
16 November 2018

17. Experimental Setup Omni-Font Correction
Correction was tested in two ways: 1. Effect on Error Reduction 2. Effect on Information Retrieval
ZAD was used for the experiments, and another collection (AFP (TREC)) was used to test the system in a different domain (news domain)
Two LMs were used to test the AFP collection: 1. LM built from same time period of AFP 2. LM built from different time period of AFP
All results were compared to previous work in correction using the character error model
16 November 2018

18. Effect of Error Reduction
Omni-Font Correction
Effect of Error Reduction
WER
Error
Reduction
Original OCR version
39% NA
Uniform Character Model
17%
56%
Trained Character Model
12%
70%
ZAD
WER
Error
Reduction
Original OCR version
31% NA
Uniform
Character
Model
AFP LM
7.3%
76%
News LM
11.7%
62%
Trained
5.9%
81%
10.7%
65%
TREC
16 November 2018

19. Results in MAP for searching different versions of the ZAD collection
Omni-Font Correction
Effect of Retrieval
Results in MAP for searching different versions of the ZAD collection
16 November 2018

20. Integrated System Outlines
Possible Implementations
Detailed Implementation
Experimental Setup
Results
16 November 2018

21. Possible Implementations
Integrated System
Possible Implementations
Correction
Fusion
Degraded Text
Less degraded Text
Fusion
Correction
Degraded Text
Less degraded Text
16 November 2018

22. Implementation Integrated System Fusion Correction
Degraded Text Version1
Degraded Text Version2
Fused Text Version
Much lower errors version Text
Fusion
Correction
Degraded Text Versionn
16 November 2018

23. Experimentation Setup
Integrated System
Experimentation Setup
Correction was applied on all fused versions of text shown in fusion section (versions of ZAD)
Correction was applied in two different manners: 1. Whole text correction 2. OOV text correction
For fused versions, 55% of WER are OOV
16 November 2018

24. Results Integrated System 0.9% 1.3% 26.3% Version Fused error rates
WER after Correction
Error Reduction
OOV Cor.
Full Cor.
K.200
8.7%
7.1%
7.9%
18.9%
9.1%
K.300
6.1%
5.1%
7.2%
15.6%
-18.7% K
5.0%
4.1%
6.2%
16.8%
-25.7%
M.200
3.6%
2.4%
4.8%
32.6%
-32.8%
M.300
1.9%
0.9%
3.7%
52.7%
-96.5% M
2.0%
1.0%
3.8%
47.9%
-88.4%
S.200
11.9%
9.4%
9.5%
20.6%
19.8%
S.300
2.5%
1.3%
48.7%
-54.0% S
2.1%
1.8%
4.3%
15.1%
-103.8%
RDI.K
4.0%
3.3%
19.0%
-53.7%
RDI.M
1.5%
39.9%
-68.6%
RDI.S
39.8%
-9.1%
Sakhr.K
34.7%
29.7%
26.3%
14.6%
24.1%
Sakhr.M
5.8%
34.2%
-6.4%
Sakhr.S
3.4%
4.4%
41.6%
-29.6%
Version
Least Original WER
New WER
Error Reduction
M.300
3.0%
0.9%
70.0% M
1.0%
66.7%
RDI.M
1.5%
50.0%
K.300
5.6%
5.1%
8.9% K
4.1%
26.8%
RDI.K
3.3%
41.1%
M.200
8.8%
2.4%
72.7%
Sakhr.M
3.8%
56.8%
S.300
9.1%
1.3%
85.7% S
1.8%
80.2%
Sakhr.S
2.0%
78.0%
RDI.S
9.4%
74.5%
K.200
14.6%
7.1%
51.4%
S.200
16.5%
43.0%
Sakhr.K
41.8%
29.7%
28.9%
16 November 2018

25. Conclusion (1/2) Text fusion proved to be mostly effective
Fusion of two OCRed text of the same image with different resolution improves text quality
Omni-font correction proved its effectiveness on error reduction
Using trained character error model have better effect on error reduction than uniform one. However, both has indistinguishable IR effectiveness.
16 November 2018

26. Conclusion (2/2)
The key for better correction is using a well trained language model
An integrated system that comprise the effectiveness of text fusion and error correction proved its ability on achieving a significant reduction in errors:
Average Error Reduction = 56%
Max. Error Reduction = 86%
Max. Theoretical Error Reduction: WER = OOV
16 November 2018

27. Possible Future Directions
Applying Text Fusion on character level instead of word level
Applying different implementations for the integrated system
Using Factored language model
Applying all experiments for different types of degraded text, such as ASR
Testing the usage of a huge amount of data for creating a general LM instead of a domain specific LM
16 November 2018

28. بزاكم الته خيرا
جزاكم الله خيرا
جزاكم الله خبرا
16 November 2018