1. UN Workshop on Data Capture, Minsk Session 15 Data Capture Process with Optical Character Recognition Image Character Recognition Intelligent Recognition
Christoph Steinl Vice Director International
Enterprise Content Management

2. OCR Optical Character Recognition
Agenda
OCR Optical Character Recognition
ICR Image Character Recognition
DFR Dynamic Form Recognition
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3. OCR = optical character recognition
Technology was first invented in 1929
Gustav Tauschek obtained a patent on OCR in Germany
Mechanical device that used templates
First commercial system was installed at Readers Digest in 1955
Years later donated to the Smithsonian Institution
Today
Recognition of machine written text is now considered largely a solved problem
Accuracy rates exceed 99%
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4. OCR
Beta Systems well experienced with this recognition engines in Banks
in Germany OCR A ⑁ Chair ⑀ Hook ⑂ Fork
Austria OCR B Plus
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5. ICR Image Character Recognition
The technique is far ahead of OCR because of ongoing development of ICR
Handwriting recognition system
Allows different styles of handwriting to be learned by a computer during / before processing to improve accuracy and recognition rates
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6. ICR Process: Capturing the image with Scanners
Processing by (ICR) and/or (OCR)
Segmentation is a very important step
Decision if the homogenous criteria belong to the foreground or to the background
Human editors can do that depending on the context
Comparable to computer tomography: according to different results from radio waves reflected from different angels the computer can reconstruct the picture
With the first step only a suitable starting point (sets of pixels) is possible
The increasing process links all closer pixels (computation of valleys and peaks with high degree of confidence)
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7. ICR Process:
Pre-processing
Deskew
Shift, rotate
Stretch
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8. Recognition – Image Pre-processing
Skewed
document ...
…after alignment
Stand / Bearbeiter: (Sing / Kleber / Reim)
Status: freigegeben
Einsatzbereich: Beta Systems
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9. ICR Process: Less / More Contrast
Enhance
Less / More Contrast
Clean up (de-noise, halftone removal)
to enable the recognition engine to give best results
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10. Recognition: Noise and box removal
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11. Classification A one was written 90 % = 1 8 % = 7 2 % = 4 ICR Process:
90 % = 1
8 % = 7
2 % = 4
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12. ICR Algorithm:
Neural Network
Using kNN k-Nearest Neighbour
SVM Support Vector Machine Minimize simultaneously the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers
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13. ICR Process:
After different classification alternatives the appropriate confidence will be provided
Recognition Limitation only for most probable characters e.g. if only characters 3,6,0 are possible the engine can also be limited to this set and the results are much better
Voting Machine
Usability:
security,
efficiency and
Accuracy
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14. Dynamic Field Recognition
No fixed position is required
If form is only ½ available still ½ readable
No special Forms are required
No timing tracks are necessary on the forms for OMR but results are also available the same time no cleaning of LEDs in the scanner necessary
Robust against vertical / horizontal stretching, shrinking and displacement (e.g. Variation in printing)
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15. Dynamic Field Recognition
Recognizes:
features (word as pixel cloud)
boxes,
lines and
symbols
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17. Hardware- / Software - Requirement
Scanner PC
Network
Disc Storage necessary for re-processing and if images are needed for audit purposes
Software
Scan Software
One Recognition and Voting Software for OMR, OCR, ICR, Barcode
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18. Cost Comparatives in general OMR/ICR from dedicated OMR Scanner
OMR/ICR from image
OMR/ICR from dedicated OMR Scanner
Forms Design
Same
Forms Production -
Up to 50% More
Enumerator Training
Up to double the cost
Scanners PC
Low cost PC
PC Operators
Servers
Cost of more/new flexibility
low
high
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19. ICR Advantages Better than: Manual keying
90 % (plus) correct keys Manual = higher substitution rate than automated recognition
Time consuming
Deliberate manipulation possible
OMR, because OMR is space consuming
OCR, because OCR is machine written and therefore of limited use
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20. ICR Advantages
Clear accuracy for OMR because of dirt removal by software depending on the mark size and figure
Can detect line and can ignore dirt
Clear result
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21. ICR Advantages Barcode, OCR OMR, and ICR Recognition with one Software
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22. ICR Advantages
Pro:
Only rejected characters/fields need correction Rest of the form untouched
With new technologies open for future faster, better quality
With standardized correction mode
Handwriting of the corresponding country will be recognized
The previously mentioned advantages do not have to be repeated here again
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23. ICR Advantages : Capture Process
SORM Scan Once Read Multiple
Images are Scanned once and stored for re-processing. (disk space is cheap)
In several serial sessions parts of the data is collected from the Image (important fields first).
Example: SORM Session 1: Fields Age, Sex and Nationality -> provisional partial results SORM Session 2: All other numeric fields SORM Session 3: Alphanumeric fields that need more manual coding (Occupation -> Occupation Code)
Each Session Updates the Data files / Database until all data is captured.
Faster preliminary results. Less political stress.
Faster data for PES planning
Analysis of Session 1 results is possible in parallel to Recognition, Coding and Editing of Session 2
Data lifting on different batching levels is possible. (EA, settlement)
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24. Process Stages of Census Surveys
Christoph J. Steinl, Vice Director int. ECM
December 2008, Minsk

25. Capture Process
Store In (EA Batch Header Creation – EA Paper store Database)
Scanning
Recognition
Verifying Processes
The solution
Data capture
Census Process internal
Census data flow
Quality assurance
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26. Scanning Kleindienst SC80HC
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27. Scanning Simultaneous creation of up to six images
Optical lens > 10 mm, sharpness-depth-area 3 mm
Optical und ultrasonic double feed control
Energy saving / live cycle extending Mode
Consistent jam handling: no document is lost or double captured due to physical jams
cleanness check program: detects white - and black dirt spots
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28. Scanning Pockets 2 – 12 why:
if the document is scanned skewed or de-skewed
if the very important questions are filled / are readable (if OMR, OCR, Barcode)
if there are fingerprints on the questionnaire or not
if the Barcode/OCR/OMR numbers (not ICR) numbers are in the given range
if there are double entries – we check the unique number
if there are (colour) copies used
if there are mismatches in quantity: Batch header shows 50 and only 40 are scanned
Transport stop can be programmed to clarify the issue
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29. Scanning
Customer:
I have a printer and print since long my own questionnaire
…I learnt from the internet that it is just a matter of software …
Before printing we should be consulted to give best advice, we will test and optimize.
Single side printing or higher scaled paper is necessary (shine through factor = opacity)
Paper should be white without any spots inside
Discuss different methods before making big investments
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30. Census Process internal
Form Type Analysis
Structure Analysis
ICR voting
Batch Job Processing
ICR 1
ICR 2
Editing / Coding
Logical Result Analysis
ICR Result Analysis
Output Assembly
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31. The Path to Recognition
Analyze the structure of documents for identification
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32. The Path to Recognition
Perform proper clean-up and image pre-processing
Analyze individual page layout
Dynamically locate fields of interest
Character recognition :numeric handwriting voting of two ICR Engines and also with OMR.
Compile results
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33. Verifying Processes Unique Number – double Scan check
Double feed check
Check if Copy
Trace of all editing work
Logical checks
Completeness checks
Reports
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34. The Solution: SC80HC + FC Census
FC Census recognition
DevInfo
Work
Data
Storage & DB
Data + Images
Preparation:
cut & jogg
CSPro
Export
Batch Header
Archive
Data
Storage & DB
Paper
Archive
Formx
Redatam
TAPE
Editing
Local reports
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35. Data capture Data Processing Centres in different locations
Peak period 3 shifts, average 1-2 shifts
Local operators trained by our supervisors
Supervisors local
Central support from Lab
Training & documentation realised in advance
Help to design the documents
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36. Thank you for your attention
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