OCR and OCV. Tom Brennan Artemis Vision Artemis Vision 781 Vallejo St Denver, CO (303)

Transcription

1 OCR and OCV Tom Brennan Artemis Vision Artemis Vision 781 Vallejo St Denver, CO (303)

2 About Us Machine Vision Integrator Turnkey Systems OEM Vision Software Work with camera partners and their clients Tom Brennan Artemis Vision 781 Vallejo St. Denver, CO (303)

3 OCR and OCV Considerations for Deployment OCR vs OCV Technical Challenges Pre-Processing Segmentation Recognition

4 Written Language and Machine Vision Written Human Language Highly varied: Character based and letter based Fonts and Scripts Scale, Spacing, Directionality Machine Vision Doesn t like variability: Difficult to test without stepping through examples Greater variability = greater costs

5 Barcodes vs Human Language Barcodes Highly regular Designed for Vision Readability Uniform global specifications Written Human Language Evolved over time Highly variable Many Languages, many fonts, many standards

6 OCR Applications Space or process constraints preclude barcode Human Readability Requirements Aesthetic concerns Too many legacy parts / labels in circulation Information cannot be readily barcoded (i.e. labelled drawing, or chart)

7 To OCR or Not to OCR? The barcode exists because OCR is difficult. A A OCR is typically used as a modern Turing Test

8 Hardware Setup Geometric Constraints Fixture text consistently in front of the camera Minimum 20x40 pixels per character Diffuse lighting avoid hotspots light scene evenly Correct for lens distortion or longer focal length preferred

9 OCR Fonts OCR fonts minimize segmentation and recognition challenges OCR-A Characters evenly spaced Characters slightly modified to all look unique Used on Bank Checks OCR fonts are engineered for easy OCR

10 OCR and OCV Considerations for Deployment OCR vs OCV Technical Challenges Pre-Processing Segmentation Recognition

11 OCR vs OCV OCR Optical Character Recognition Attempts to read text OCV Optical Character Verification Verifies text conforms to a standard Helps diagnose printer problems Missing Lines Low contrast

12 OCV Typically verifies known text Difficult to combine OCV and OCR. Smudged 6 or Good 8 OCV for lot code verification, expiration date verification, etc.

13 OCR and OCV Considerations for Deployment OCR vs OCV Technical Challenges Pre-Processing Segmentation Recognition

14 OCR Steps Pre-process Reduce background noise Improve characters Segment Locate and divide into characters Recognize Identify Specific Characters

15 Pre-Processing Reduce Noise Erosion and Dilation Adaptive Thresholding Blur and sharpen Improve Character Consistency Compute Skeletons Compute Stroke Width Prune

16 Noise Reduction Techniques Dilation Expansion of light colored areas Erosion Shrinking of light colored areas Original Dilated Eroded

17 Character Consistency Skeleton All points equal-distant from at least 2 edges Think start a fire on the boundary, where fires meet, draw a point

18 Locating Text Easy for people. Can be a challenge for software. Logos Symbols Lines OCR applications will work best when text is consistently located.

19 Segmentation Splitting Text into Discrete Characters Critical to accurate OCR Issues Not all characters are the same width Not all characters can be split with vertical lines due to skew Sometimes characters touch

20 Segmentation Under-segmentation Over-segmentation

21 Segmentation Adaptive Thresholding Detect Corners Estimate Stroke Width Edge detection Path detection

22 Recognition Can be easier than Locating and Segmenting However Similar Characters: l, 1, I, i, 7, /, \, (, ) B, D, 8, 6, 9, S, Z, R, P Handwriting vs Type Scale and Orientation (Document Scan vs. Package on Conveyor)

23 Recognition Strategies Pattern Matching Techniques Match the actual image pattern Can be problematic on large character sets Artificial Intelligence Techniques Extract Features from the image Learn rules for features Neural nets, SVMs, knn, AdaBoost, etc. Tesseract uses a feature distance method

24 Context? Can we use context to aid recognition?

25 Integrated Approaches Poor match score: Re-segment and re-match:

26 Conclusions General Purpose OCR is challenging Consider shortcuts to make OCR easier Context? Character number known? Character size known? Font known? Can we train on that font? Eliminate hotspots, distortion Locate text consistently, control scale, orientation Preprocess to improve image / characters

27 Questions? Tom Brennan Artemis Vision 781 Vallejo St Denver, CO (303)