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Learn about OCR: Optical Character Recognition. About Your Presenter. Presenting today: Juan Worle Technical Training Coordinator Microscan Corporate Headquarters Renton, WA. Course Objectives. By completing this webinar you will: Understand definition of OCR - PowerPoint PPT Presentation
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Track, Trace & Control Solutions© 2011 Microscan Systems, Inc.
Learn about OCR: Optical Character Recognition
Learn about OCR
© 2011 Microscan Systems, Inc.
About Your Presenter
Presenting today:
Juan WorleTechnical Training CoordinatorMicroscan Corporate Headquarters
Renton, WA
Learn about OCR
© 2011 Microscan Systems, Inc.
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
Learn about OCR
© 2011 Microscan Systems, Inc.
Topics
About OCR OCR and OCV Decoding OCR Example applications
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© 2011 Microscan Systems, Inc.
About OCR
What does OCR mean, and some perspective
What is OCR? A little history OCR and Machine Vision
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© 2011 Microscan Systems, Inc.
What is OCR?
Optical Character RecognitionThe conversion of written or typed text into a string of characters formatted
for machines.
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© 2011 Microscan Systems, Inc.
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
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© 2011 Microscan Systems, Inc.
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
Automatic check processing machines use OCR algorithms and MICR fonts
The first patents were developed in the 1930s by Gustav Tauschek and then Paul Handel
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© 2011 Microscan Systems, Inc.
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
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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 partsConsistent lighting and environmentConsistent fonts
Example of LOT and DATE codes:By reading the text, the date can be checked, and the lot number verified
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© 2011 Microscan Systems, Inc.
OCR and Machine VisionThere are three uses for OCR and OCV tools.
OCR is used to identify the contents of unlabeled cans
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
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© 2011 Microscan Systems, Inc.
OCR and OCV
Understand the difference between Recognition and Verification
Comparison of OCR and OCV Methods to read OCR
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© 2011 Microscan Systems, Inc.
Comparison of OCR and OCV
OCV: Optical Character VerificationUse 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 RecognitionThe 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
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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
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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
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© 2011 Microscan Systems, Inc.
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.
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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.
When using trainable font tools, a character is not recognized until it is trained
Trainable OCR tools let you use a non-standard font
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Decoding OCR
Understanding the unique traits of an OCR font
Character dimensions Font characteristics Print variations Improve performance
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Character Dimensions
The overall size of the character matters, as well as the features.
AHeight
Width
Features
Area
Line Weight
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The font matters. Maximize the difference in similar characters for more reliability. Many characters have very little difference.
OC Q G
Font Characteristics
OLet’s look at the Arial font. The Character O has many similar characters:
O OOriginal Letter C
80% matchLetter Q
75% matchLetter G
70% matchArial: High probability of confusion
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© 2011 Microscan Systems, Inc.
Fonts designed for machine reading work best. Uniform character spacing Each character designed to be different than all others DPM applications
Font Characteristics
Low probability of confusion: OCR-A is designed for machine reading and has differences in similar characters
Verdana Sample
OCR Sample
Even character separation improves readability
There are also several fonts designed for Inkjet and Direct Part Mark (DPM) applications
Good:
But better:
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© 2011 Microscan Systems, Inc.
Print Variations
Even if the text looks good on screen, printers can change appearance.
SKEWANG EL
SCALE
LINE WEIGHT
DEFECTS
Print considerations DPM considerations
Dot Size
Overprint Underprint
Dot Spacing
Skew
Dot offset
Tip: Avoid large gaps when marking characters.
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© 2011 Microscan Systems, Inc.
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
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© 2011 Microscan Systems, Inc.
Improve PerformanceThere 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
Morphology: Vision tools that can improve the appearance of an image
Teach variations of the font for more reliable reading
Dynamic location is helpful if the part location moves
De-focus
Line weight
Line weight
Original
– Slight rotations– Focus
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© 2011 Microscan Systems, Inc.
Improve PerformanceRecommendations 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”)
A BIdeal space between characters
is half the character size
Feature size > .015”
25 Pixels
30 Pixels
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© 2011 Microscan Systems, Inc.
OCR Applications
Some common OCR applications
Print verification Label verification Date and Lot code tracking Part identification
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Print Verification
Continuous ink-jet (CIJ) on cartons. Validate the data Combine with barcode tool Feedback when the head should be cleaned
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© 2011 Microscan Systems, Inc.
Label Verification
Ensure the proper label is applied. Several products are run on a single line Report error when incorrect label is applied
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© 2011 Microscan Systems, Inc.
Date and Lot Code Tracking
Date and Lot code traceability. Validate and verify printing Track products through manufacturing Conform to regulations (FDA)
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© 2011 Microscan Systems, Inc.
Part Identification
Identify gasket for installation.Read the part number to ensure the correct part is installed
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© 2011 Microscan Systems, Inc. © 2011 Microscan Systems, Inc.
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
Learn about OCR
© 2011 Microscan Systems, Inc.
Thank you!For more informationWebsite: www.microscan.com
– Online courses– Spec sheets– Technology brochures– Support self-help and support request form
Instructor:Juan Worle, Technical Training CoordinatorEmail: [email protected]
Feedback on this webinar: www.microscan.com/feedback
Additional contacts:Product information: [email protected]: [email protected]: [email protected]
Graduation exerciseDownload Visionscape from www.microscan.com download center
