Pre-SWOT Report. Printed Arabic OCR

1. Pre-SWOT Report.Printed Arabic OCR Dr. Mohamed El-Mahallawy Eng. Hesham Osman Eng. RanaAbdou Dr. Mohamed WaleedFakhr Dr. MohsenRashwan

2. 1-Introduction and challenges • These systems recognize text that has been previously written or printed on a page and then optically converted into a bit image. Offline devices include optical scanners of the flatbed, paper fed and handheld types. • Arabic printed script is more difficult than Latin script for the following reasons:

3. challenges • Connectivity problem: segmentation and recognition • Dotting problem • Multiple Grapheme shapes depending on the position. • Ligatures: To make things even more complex, certain compounds of characters at certain positions of the Arabic word segments are represented by single atomic graphemes called ligatures. • Overlapping problem • Diacritics problem • Fonts families and size variations: نسخ، كوفى، رقعة Each has sub-families (Mac versus Windows) • Finally, the font size problem: Different Arabic graphemes do not have a fixed height or a fixed width. Moreover, neither the different nominal sizes of the same font scale linearly with their actual line heights, nor the different fonts with the same nominal size have a fixed line height.

4. 2- Applications • Digitizing billions of books for digital library storage, archival, retrieval, and classification. • Digitizing historical documents

5. 3- State of the art in products (Latin script) • OCR is a highly mature technology for Latin script with excellent performance. • The main challenges are in the pre-processing, page segmentation, speed of batch processing and post-processing. • OmniPage-17 by Nuance is an example of such a product with less than 1% WER: http://www.nuance.com/imaging/omnipage/omnipage-professional.asp

6. 4- State of the art in products (Arabic script) 1- Sakhr: 1% WER for good quality documents but may drop significantly with poor quality documents. (Best speed, and best output layout) 2- VERUS: a little lower than Sakhr for good quality but significantly better for poor quality. • (bibliotheca alexandrina uses both engines for its digitization project). 3- Readiris: Lower performance than the other two.

7. 5- State of the art in Research and Competitions • Focus mainly on producing true Omni OCR for different font families, font sizes (specially the large), document pre-processing and framing, noise robustness, and batch-mode speed. • Significant recent efforts: Most recent research employ HMMs, and fusion between multiple OCR systems targeting Omni font performance.

8. 6- Required Modules • ScanFix pre-processing tool (or similar): 15$ per license. • Nuance document analysis tool (Framing tools) (or similar): 30$ per license. • Word based language model • Character based language model • Grapheme to ligature and ligature to grapheme convertor: Need to build a tool • Statistical training tools: HTK, SRI, Matlab, and many neural network tools. • Error analysis tools: Need to be implemented. • Diacritic Preprocessing tool • Language Recognition tool

9. 7- Required Resources • Word annotated corpus (estimated 5000 pages of different quality-resolution and font styles). • Character/Ligature annotated corpus for initial models (estimated 8 pages covering all shapes, with about 25 instance per shape). • Character-based language models (use digital resources). • Word-based language models (use digital resources). • Dictionaries with transcriptions

10. 8- Available Resources and Gaps • We need some tools to be available (error analysis, grapheme to character/ligature, pre-processing). • No available database so we need to do data collection very soon. • Character/Ligature-based Language models have to be trained and made available for researchers.

11. 9- LR proposed by ALTEC:Training • We need to focus on the Naskh fonts family. Within Naskh, there may be about 6 families. Each would have 6 different font sizes (8,10,12,14,16,18). The rule is to have about 25 instances for each shape in each case. • We assumed to have about 300 different shapes (characters and ligatures). So we need 300*25=7500 instances. This is about 8pages. • This should be done for each font family and for each font size as follows: • 8pages*6faontsfamilies*6fontsizes= around 300 pages total (Clean=Excellent Quality).

12. LR proposed by ALTEC (cont.) • These pages (for clean high quality training data) will be generated artificially, by balancing the data to cover all the 300 shapes.  • Then, to generate lower quality training data: • a- The 300 pages will be outputted from a Fax machine (once) • b- The 300 pages will be copied once (one output), then twice (second output). • c- The same process will be done for 600, 300, and 200 dpi. • (This gives 3600 pages: 300 clean, 300 from Fax, 300 copied once, 300 copied twice) multiplied by 3 for the 3 different resolutions. • We will also obtain 2000 transcribed pages from Alex. Bib. with low quality old books, etc.).

13. LR proposed by ALTEC(Benchmarking) • The recommended Benchmarking must be two-folded; one is to measure robustness and reliability of the product (software) and this requires 40,000 documents in one batch. These should include simple and complex documents, different qualities, etc. • The second test, for accuracy, should include at least 600 pages (200 high quality, 200 medium, and 200 poor quality) coming from books, newspapers, Fax outputs, Typewriters, etc. • It is highly recommended to have an OCR competition co-organized by ALTEC.

14. 10- Preliminary SWOT analysis • Strengths: • The expertise, in DSP, pattern recognition, image processing, NLP, and stochastic methods • Potential to have huge amounts of annotated data. • Weaknesses: • The tight time & budget of the intended required products. • No benchmarking available for printed Arabic OCR • No training database available for research community for Arabic OCR

15. Opportunities: • Truly reliable & robust Arabic Omni OCR systems are a much needed essential technology for the Arabic language to be fully launched in the digital age. • No existing product is yet satisfactory enough • The Arabic language has a huge heritage to be digitized. • Large market of such a tech. of over 300 million native speakers, plus other numerous interested parties (for reasons such as security, commerce, cultural interaction, etc.). • Threats: • Back firing against Arabic OCR technologies in the perception of customers, due to a long history of unsatisfactory performance of past and current Arabic OCR/ICR products. • Other R&D groups all over the world (esp. in the US) is working hard and racing for a radical solution of the problem.

16. 11- Survey •  Specify the application that OCR recognition will be used for • What is the data used/intended to train the system? • What is the benchmark to test your system on? • Would you be interested to contribute in the data collection. At what capacity? • Would you be interested to buy Arabic OCR annotated data? • Would you be interested to contribute in a competition • How many persons working in this area in your team? What are their qualifications? • What are the platforms supported/targeted in your application? • What is the market share anticipated in your application? • Would your application support any other languages? Explain.

17. List of Survey Targets • Sakhr • RDI • ImagiNet • Orange- Cairo • IBM- Cairo • Cairo University • Ain Shams University • Arab academy (AAST) • AUC • GUC • Nile University • Azhar university • Helwan university • Assuituniversity • Other Centers outside Egypt • Other companies that are users of the technology

18. 12- Key Figures in this Field • NovoDynamics (VERUS) research team: Dr. Steve Schlosser et. al. • Dr. John Makhoul (BBN) • Dr. HazemAbdelAzeem (Egypt)