The document provides a comprehensive overview of Optical Character Verification (OCV) and Optical Character Recognition (OCR), detailing their definitions, applications, and underlying technologies. It discusses the comparison between OCV and OCR, emphasizing their different focuses on quality verification versus text recognition, and outlines the critical considerations for effective implementation, such as font types, illumination, and validation approaches. Additionally, it covers non-functional aspects including usability, accuracy, and flexibility needed for successful OCV/OCR system performance.

2. TABLE OF CONTENTS
1. INTRODUCTION TO OCV/OCR
2. APPLICATIONS OF OCV/OCR
3. UNDERSTANDING YOUR TEST ENVIRONMENT
4. VALIDATION APPROACH TOWARDS OCV/OCR SYSTEMS

3. INTRODUCTION
What is OCV & OCR?
→It is conversion of printed, documented or handwritten text into machine
encoded/readable form.
→It is a common method of digitizing printed texts so that they can be
electronically edited, searched, stored more compactly, displayed on-line,
and used in machine processes such as cognitive computing, machine
translation, (extracted) text-to-speech, & text mining.

4. COMPARISON OF OCV & OCR
OCV : Optical Character Verification
→ There is a prior knowledge about what the specific alphanumeric
pattern is and the system is essentially verifying or validating that the
current character is exactly what it is supposed to be
→The output of an OCV is
quality report of correctness.
OCR: Optical Character Recognition
→An alphanumeric character is being recognized based on a comparison
to essentially a database of character patterns.
→The output of an OCR is
machine usable text.
OCV: verify quality
OCR: read text

5. COMPARISON OF OCV & OCR
Verification:
→Inspecting characters for content, correctness, quality, contrast and sharpness
compared to stored templates.
Examples:
• Date / Lot verification
• Component ID verification
• Label, carton
• 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

6. Reading:
→A tool for reading text strings of random content and converting to machine
usable text.
Examples:
• Sorting and identification
• Serialization
• 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
COMPARISON OF OCV & OCR

7. CLASSIFICATION OF OCV/OCR SYSTEMS
→NORMAL - Normal OCR/OCV refers to detection of standard computer
generated/printed characters.
→ADVANCE/INTELLIGENT - Intelligent system refers to detection of
handwritten cursive text.

8. APPLICATIONS OF OCV/OCR
• Data entry for business documents
• Automatic number plate recognition
• Pen computing
• Assistive technology for blind/visually impaired users
• Track & trace of goods throughout the supply chain

9. UNDERSTANDING TEST ENVIRONMENT
• Font type
• FOV
• Vision sensor(camera)
• Illumination(optics)
• Image Focus
• Aspect ratio
• Line Weight/Stroke Width

10. FONT TYPES
→Fixed Fonts: A font whose letters and characters each occupy the same
amount of horizontal space. This 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.
→Trainable Font: Any font can be presented and learned by Machine Vision
software during setup, then identified during run-time.
Examples:
• DOTTED
UNDERSTANDING TEST ENVIRONMENT
When using trainable font tools, a character
is not recognized until it is trained

11. Why font type matters?
→ Fixed fonts are designed to ensure precisely specified sizing, spacing, and
distinctive character shapes which improves accuracy, reliability &
reduces processing time.
→ Incase of other fonts character width would be varying.
UNDERSTANDING TEST ENVIRONMENT

12. Fixed Fonts provide
→Uniform character spacing
→Each character designed to be different than all others to maximize
difference in similar characters .
Example: Consider character O which has many similar characters such as
Q,C & G.
OCR-A
ARIAL
UNDERSTANDING TEST ENVIRONMENT

13. Field of View
→ It is the area in the eyepiece of an optical instrument in which the image is
visible.
→FOV will directly influence maximum no of characters that can be
detected/decoded under the given camera & lens.
Large FOV → Large data
Small FOV → Small data
UNDERSTANDING TEST ENVIRONMENT

14. Vision Sensor(Camera)
→For a given FOV, camera resolution directly influences resolution i.e.
character width & height in terms of pixels.
→Higher the camera resolution, higher the no of pixels assigned to
characters. Higher no of pixels further increases performance.
UNDERSTANDING TEST ENVIRONMENT
25 Pixels
30 Pixels

15. Illumination
→Illumination distinguishes foreground from background.
→Illumination should be uniform throughout the FOV & strong enough such
that higher contrast is generated between foreground & background.
UNDERSTANDING TEST ENVIRONMENT

16. Image Focus
→Before running the system user must ensure that image is focused such
that character edges are sharp.
UNDERSTANDING TEST ENVIRONMENT

17. Aspect Ratio
→It is a ratio of width to the height of a character.
→It does not influence character decoding. It is a pre-condition i.e. technical
specification of OCV/OCR systems specify minimum character width, height
system can decode.
UNDERSTANDING TEST ENVIRONMENT
Height
Width

18. Line Weight/Stroke Width
→The weight of a particular font is the thickness of the character outlines
relative to their height.
→As a rule of thumb, stroke width of the smallest character should be at least
three pixels wide.
→Specification of an OCV/OCR system should specify minimum width system
will be able to decode. It is specified in terms of pixels.
UNDERSTANDING TEST ENVIRONMENT
Line
Weight

19. →Apart from functional, OCV/OCR systems should be validated focusing more
on non-functional points.
→Considering area of application an OCV/OCR system should be validated for
1. Sensitivity and Specificity
2. Usability
3. Accuracy
4. Flexibility(Pattern Matching)
VALIDATION APPROACH

20. Teach & Inspection
→Once all characters are taught system must apply teach data correctly in
inspection.
→As long as the printing specification remains same as taught characters,
characters should be decoded correctly.
VALIDATION APPROACH - FUNCTIONAL

21. Decode string & status
→For each image inspected system must reflect the decoded string along
with status.
→If string is decoded correctly it should be reflected in GREEN or else if it is
failed then it should be reflected in RED.
VALIDATION APPROACH - FUNCTIONAL

22. Verification score
→For each character read or measured by system , a verification score is
assigned to the character.
→In Inspection system compares the measured score of each character to
the reference score set in teach.
→If measured score < reference score character should be Failed & if
measured score > reference score character should be Passed.
→System should allow user to set value of reference score for each character
in Teach as well as Inspection.
VALIDATION APPROACH - FUNCTIONAL

23. Consistency & Reliability
→ An OCV/OCR system must produce similar results under consistent
environment.
→ Environment comprises of optics(light & polarizer) & vision
sensors(camera & lens).
Test Method: Keeping carton steady at a position under the camera, tester
can generate triggers & observe character decoding for each trigger.
Trigger count can be kept at 100.
VALIDATION APPROACH – NON FUNCTIONAL

24. Usability
→An OCV/OCR system should be usable enough to gain confidence of the
end user.
→Following points must be considered by a tester from usability perspective.
1. System should be very easily programmable
2. System should be assistive, preventing user from false teach
VALIDATION APPROACH - NON FUNCTIONAL

25. Accuracy & Precision
→An OCV/OCR system is considered valid if it is both accurate & precise. A
tester must validate the system for accuracy & precision.
→As per the industry standards OCV/OCR systems are considered best when
accuracy of 99.9% is given.
→System should be robust enough to handle confusion pairs irrespective if
any font type such as 'B' with a '3' or '8'; letter 'I' with a '1'; 'G' with '6';
the letter 'O' with the numeral zero; '6' with '9' or '3' with'8‘.
VALIDATION APPROACH – NON FUNCTIONAL

26. Flexibility (Pattern Matching)
→System should not be restrictive. It should be flexible enough to
accommodate possible variations.
→These variations can be in terms of print orientation & product alignment
along vertical & horizontal direction.
→Print orientation variation can be handled through functionality of image
rotation.
→Product alignment variation can be handled through pattern matching.
VALIDATION APPROACH – NON FUNCTIONAL

27. VARIATIONS DURING INSPECTION & WAYS TO TACKLE
THEM
• Carton alignment variation
• Print alignment variation
• Print quality variation

28. Carton alignment variation
→During inspection position of the carton may vary along horizontal or
vertical direction with respect to its original teach position.
→These positional variations can lead to false rejection in inspection.
→System shall be able to tackle such a situation through Pattern Matching
option.
→Pattern matching enables the system to read character string at any
position within FOV.
VARIATIONS DURING INSPECTION & WAYS TO TACKLE
THEM

29. Print alignment variations
→There may be products with up side down printing. Position of the camera
with respect to printed characters may not be aligned.
→Through Image Rotation functionality such a scenario can be handled.
→Provision should be there within system to teach & inspect images with
rotation of 90,180,270 degrees.
VARIATIONS DURING INSPECTION & WAYS TO TACKLE
THEM

30. Print quality variations
→In case of OCV/OCR system along with on line printing, quality of print may
deteriorate after certain time period.
→Variation in print quality also leads to false rejection.
→Such variations can be handled by reducing verification score during live
inspection.
→Integrity & readability of the data must be ensured even if verification
score is reduced.
VARIATIONS DURING INSPECTION & WAYS TO TACKLE
THEM
Dot Size
Overprint Underprint