1. Fusion of Multiple Corrupted Transmissions and its effect on Information Retrieval
Walid Magdy Kareem Darwish Mohsen Rashwan

2. Outlines Motivation Prior work Fusion Definition Approach
Experimental Setup
Results
Conclusion & Future work

3. Motivation Many Arabic documents are available only in print form.
The need of transforming these documents into electronic form increased since the end of last century, where searching E-text is much easier.
Arabic OCR accuracy is still much lower than the state-of-the-art for other languages, such as English.
Degraded text, resulting from OCR systems, affects the effectiveness of Information Retrieval.
The need for having higher quality text for Arabic documents became a must for improving IR effectiveness.

4. Prior Art: Previous work on OCRed text focused on two main aspects:
Work involves improving Information Retrieval effectiveness regardless of improving text quality.
Work focuses on improving text quality leading to improvement in IR effectiveness.
Examples:
Query garbling based on character error model.
OCR correction based on character error model and Language model.

5. Degraded version of 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)

6. Approach: ولا ثدي إلا في ألمستدلاله بنور4 ولا حيا4 إلا في رضا4
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
Language Model
ولا ثدي إلا في الاستدلال بنوره ولا حياة إلا في رضا5

7. Experimental Setup:
Only one OCR system was available “Sakhr Automatic Reader v4”.
In order to obtain multiple sources for a given data set:
Few pages were selected at random from a book, OCRed, then outcome text was 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 a clean text using different CER’s.
Used OCRed book for test was Zad Alma’ad, with the following specs:
Eight pages scanned at 300x300 dpi that contain 4,236 words, with CER of 13.9% and WER 36.8%.
Clean version of the book was available in electronic form that consists of 2,730 separate documents. Associated a set of 25 topics and relevance judgments.
LM is built using a web-mined collection of religious text by Ibn Taymiya, the teacher of the author of Zad Alma’ad
MAP was used as the figure of merit for IR results.

8. Experimental Setup: Generating Synthetic Garbled Data
For a clean word “قنبلة” ق
ق 0.8
ف 0.1
ت 0.05
ن 0.05
Generate random number
قـنـبـلـة
قـنـبـلـة
Garbler
تـنـبـلـة تـ
Character Error Model
0.921 ق ف ت ن
0.0
0.8
0.9
0.95 1

9. Experimental Setup: Generating Synthetic Garbled Data
k =
CERnew
CERorg ق ف ت ن
0.0
0.8
0.9
0.95 1
k = 2 ق ف ت ن
0.0
0.6
0.8
0.9 1
k = 0.5 ق ف ت ن
0.0
0.95
0.9 1
0.975

10. Experimental Setup: Generated Versions
Data set k
CER
WER
OOV
Original NA
13.9%
36.8%
20.9%
Model-1 1
36.3%
21.1%
36.4%
Model-2
0.5
7.0%
20.3%
11.9%
20.4%
Model-3
0.67
9.3%
26.1%
15.2%
25.9%
Model-4
1.25
17.4%
43.2%
25.0%
43.3%
24.9%
Model-5 2
27.9%
59.2%
33.8%
33.7%
Model Model Model Model Model-5
Error rates for generated versions
Retrieval results for generated versions

11. Results: Fusion Results
WER after fusion of both versions
Common Errors between versions
WER for outcome text from fusion process between couples of versions

12. Results: Retrieval Results
Results in MAP of searching different fused models, hashed bars refers to statistical significant retrieval results better than the original degraded versions

13. Conclusion & Future Work:
Text fusion proved to be an effective method for selecting the proper word among different candidate words coming from different sources.
Effectiveness of text fusion on WER reduction depends on the percentage of error overlap among different versions.
Information retrieval improvement as a cause of text fusion was found to be promising specially for the few outcome versions that are statistically indistinguishable from the clean version.
As a future work, fusion technique needs to be tested on real degraded data coming from different sources that will introduce a new challenge, which is word alignment among different sources.

14. بزاكم الته خيرا
جزاكم الله خيرا
جزاكم الله خبرا