Learn about OCR: Optical Character Recognition

1. Learn about OCR: Optical Character Recognition

2. About Your Presenter • Presenting today: • Juan Worle • Technical Training Coordinator • Microscan Corporate Headquarters Renton, WA

3. Course Objectives By completing this webinar you will: • Understand definition of OCR A little about the history, and where it is applied today • Know different types of OCR and how OCV is different Understand how to select the best tools • Know the critical features of OCR fonts Learn how to identify potential weak points in an application • Know how to identify reliable OCR applications Become familiar with applications that have been successful and low maintenance

4. Topics • About OCR • OCR and OCV • Decoding OCR • Example applications

5. About OCR What does OCR mean, and some perspective • What is OCR? • A little history • OCR and Machine Vision

6. What is OCR? Optical Character Recognition The conversion of written or typed text into a string of characters formatted for machines.

7. What is OCR? Optical Character Recognition • OCR fonts are unique: Unlike barcodes and 2D symbologies, they are both machine readable and human readable. • The data is considered less secure than barcodes and 2D symbols. • Some OCR software tools convert paper documents to • electronic documents. OCR conversion on a PC allows you to copy scanned text

8. A Little History OCR has been used commercially since the 1970s. • Automated bill processing: OCR systems in automated payment processing facilities • Retail check-out before UPC: Handheld OCR readers read the price of merchandise The first patents were developed in the 1930s by Gustav Tauschek and then Paul Handel Automatic check processing machines use OCR algorithms and MICR fonts

9. A Little History Today OCR is used in many specialized applications. • Search engines • Handwriting recognition • Postal tracking and document handling Google’s powerful OCR software allows you to search the web from a mobile phone Mailing systems use specialized OCR algorithms for handwriting recognition

10. A Little History OCR within Machine Vision focuses on industrial applications. • Automotive, aerospace, semiconductor manufacturing • Food and beverage handling • Packaging • Industrial applications for OCR have the following traits: • Fixtured parts • Consistent lighting and environment • Consistent fonts Example of LOT and DATE codes: By reading the text, the date can be checked, and the lot number verified

11. OCR and Machine Vision There are three uses for OCR and OCV tools. • Presence: Ensure the characters have been marked • Ensure the characters are present • Check the readability of OCR characters • Optical Character Verification (OCV) is common • Tracking: From stock through manufacturing to packaging • Lot, batch, expiration dates, serial numbers • A common barcode application • Identification: Identify part or contents of a container • Ensure proper labeling • Ensure product matches container OCR is used to identify the contents of unlabeled cans

12. OCR and OCV Understand the difference between Recognition and Verification • Comparison of OCR and OCV • Methods to read OCR

13. Comparison of OCR and OCV OCV: Optical Character Verification Use OCV tools to check the legibility and quality of text, based on a fixed and known sequence of characters. • The output of an OCV tool is a quality report of correctness. OCR: Optical Character Recognition The OCR tool is used to read an unknown sequence of characters. • The output of an OCR tool is machine usable text. OCV: verify quality OCR: read text

14. Comparison of OCR and OCV Verification: Inspecting characters for content, correctness, quality, contrast and sharpness compared to stored templates. Examples: • Date / Lot verification • Component ID verification • Label, carton, insert, outsert • Verification of on-line printing • Clinical labels • Blister packs • Direct printing on product ABC-123 Use OCV to check levels of print quality and legibility

15. Comparison of OCR and OCV Reading: A tool for reading text strings of random content and converting to machine usable text. Examples: • Sorting and identification • Serialization • Codes with Time/Date stamp • Verification of readability • decoded=readable • Verification of on-line printing • Codes with inconsistent character placement or size • Can verify text using match OCR can be used to read serial numbers on a data plate

16. Methods to Read OCR Fixed Font: The font characters must conform to a fixed pattern. Examples: • OCR-A, OCR-B: Many printed applications such as passports, documents, and pharmaceutical labels • SEMI: Used for semiconductor manufacturing • MICR: Banking documents such as checks.

17. Methods to Read OCR Trainable Font: Any font can be presented and learned by Machine Vision software during setup, then identified during run-time. • More common than fixed font because any font or variations can be trained. • Reviews each character and looks for a match in the trained font library. Trainable OCR tools let you use a non-standard font When using trainable font tools, a character is not recognized until it is trained

18. Decoding OCR Understanding the unique traits of an OCR font • Character dimensions • Font characteristics • Print variations • Improve performance

19. Character Dimensions A The overall size of the character matters, as well as the features. Area Features Height Line Weight Width

20. Font Characteristics The font matters. • Maximize the difference in similar characters for more reliability. • Many characters have very little difference. Let’s look at the Arial font. The Character O has many similar characters: O O O O C Q G Letter G 70% match Original Letter C 80% match Letter Q 75% match Arial: High probability of confusion

21. Font Characteristics Fonts designed for machine reading work best. • Uniform character spacing • Each character designed to be different than all others • DPM applications Even character separation improves readability There are also several fonts designed for Inkjet and Direct Part Mark (DPM) applications Verdana Sample OCR Sample Good: But better: Low probability of confusion: OCR-A is designed for machine reading and has differences in similar characters

22. Print Variations Even if the text looks good on screen, printers can change appearance. • Print considerations • DPM considerations • SKEW • Dot Size • Skew • DEFECTS • Underprint • Overprint • A • G • E • SCALE • N • L • Dot Spacing • Dot offset • LINE WEIGHT Tip: Avoid large gaps when marking characters.

23. Print Variations The substrate (the material you are printing on) can affect readability. • Ink absorption • Background noise • Damaged characters Background noise can cause character confusion Damaged characters and uneven surfaces can affect decodability

24. Improve Performance There are many ways to improve performance. • Use trainable font tools for more tolerance • Teach variations of a font • Line weight • Leave a quiet zone that is 2-3x character space • Use additional Machine Vision tools • Morphology: modify the image • Dynamic location: use an anchor point Dynamic location is helpful if the part location moves • Slight rotations • Focus Line weight Original De-focus Line weight Morphology: Vision tools that can improve the appearance of an image Teach variations of the font for more reliable reading

25. Improve Performance Recommendations when using Microscan products. • Use at least a 6 point font size • Adjust camera to font for 25 pixels wide/30 pixels high • Space between each character should be at least 1 point (0.015”) • Half the size of the character works best • The smallest features within a character (like Line weight) should be at least 1 point (0.015”) Feature size > .015” A B 30 Pixels 25 Pixels Ideal space between characters is half the character size

26. OCR Applications Some common OCR applications • Print verification • Label verification • Date and Lot code tracking • Part identification

27. Print Verification Continuous ink-jet (CIJ) on cartons. • Validate the data • Combine with barcode tool • Feedback when the head should be cleaned

28. Label Verification Ensure the proper label is applied. • Several products are run on a single line • Report error when incorrect label is applied

29. Date and Lot Code Tracking Date and Lot code traceability. • Validate and verify printing • Track products through manufacturing • Conform to regulations (FDA)

30. Part Identification Identify gasket for installation. Read the part number to ensure the correct part is installed

31. Learn about OCR Conclusion • The idea of OCR is not new; it has been around since the 1930s. Industrial applications gained momentum in the 1970s. • Today OCR tools are used in many non-industrial applications. Machine Vision OCR tools focus on industrial applications. • OCR tools can be categorized by OCV, OCR Fixed Font or OCR Trainable Font. • The features of a font are important and can determine the success of an application. • Font selection • Probability of confusion • Printer variables • The most common Machine Vision applications include Presence, Tracking, and Identification. • Substrate and marking method • Character separation • Character dimensions

32. Thank you! Graduation exercise Download Visionscape from www.microscan.com download center For more information Website: www.microscan.com • Online courses • Spec sheets • Technology brochures • Support self-help and support request form Instructor: Juan Worle, Technical Training Coordinator Email: jworle@microscan.com Feedback on this webinar: www.microscan.com/feedback Additional contacts: Product information: info@microscan.com Training: training@microscan.com Support: helpdesk@microscan.com