Transcript 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 ©

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
© Beta Systems Software AG 2008
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Agenda
OCR
Optical Character
Recognition
ICR
Image Character
Recognition
DFR
Dynamic Form Recognition
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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
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rates exceed 99%
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OCR
 Beta

Systems well experienced with this recognition engines in Banks
in Germany OCR A
⑁
⑀
⑂
Chair
Hook
Fork
 Austria
+
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OCR B
Plus
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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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ICR Process:
 Capturing
 Processing
the image with Scanners
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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ICR Process:
 Pre-processing
 Deskew
 Shift,
rotate
 Stretch
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Recognition – Image Pre-processing
Skewed
document ...
…after alignment
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ICR Process:

Enhance
 Less
/ More Contrast
 Clean
up
(de-noise,
halftone removal)
 to
enable the recognition engine
to give best results
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Recognition: Noise and box removal
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ICR Process:
Classification
A
one was written
90
% =1
8
%
=7
2%
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=4
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ICR Algorithm:
Neural
 Using
Network
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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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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Dynamic Field Recognition
 No
 If
fixed position is required
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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Dynamic Field Recognition
 Recognizes:
 features
(word as pixel cloud)
 boxes,
 lines
and
 symbols
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Hardware- / Software - Requirement
 Hardware
 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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OMR
Cost Comparatives in general
OMR/ICR from image
Forms Design
Same
Forms Production
-
Up to 50% More
Enumerator
Training
-
Up to double the cost
Scanners
-
Up to double the cost
PC
Low cost PC
PC Operators
Same
Servers
Same
Cost of more/new
flexibility
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OMR/ICR from
dedicated OMR Scanner
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low
high
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ICR Advantages
 Better
than:
 Manual
keying
 90
% (plus) correct keys
Manual = higher substitution rate
than automated recognition
 Time
consuming
 Deliberate
 OMR,
manipulation possible
because OMR is space consuming
 OCR,
because OCR is machine written
and therefore of limited use
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ICR Advantages
 Clear
accuracy for OMR
because of dirt removal by software
depending on the mark size and figure
 Can
detect line
 Clear
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and can ignore dirt
result
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ICR Advantages
 Barcode,
OCR
 OMR,
 and
ICR
Recognition with one Software
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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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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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Process Stages
of Census Surveys
Christoph J. Steinl, Vice Director int. ECM
December 2008, Minsk
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Capture Process
 Store
In (EA Batch Header Creation – EA Paper store Database)
 Scanning
 Recognition
 Verifying
 The
Processes
solution
 Data
capture
 Census
Process internal
 Census
data flow
 Quality
assurance
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Scanning
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Kleindienst SC80HC
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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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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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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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Census Process internal
Form Type
Analysis
Structure
Analysis
ICR voting
Batch Job
Processing
ICR 1
ICR 2
Editing / Coding
Output
Assembly
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Logical
Result
Analysis
ICR Result
Analysis
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The Path to Recognition
 Analyze
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the structure of documents for identification
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The Path to Recognition
 Perform
proper clean-up and image
pre-processing
 Analyze
individual page layout
 Dynamically
 Character
locate fields of interest
recognition :numeric handwriting
voting of two ICR Engines and also with OMR.
 Compile
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results
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Verifying Processes
 Unique
Number – double Scan check
 Double
feed check
 Check
 Trace
if Copy
of all editing work
 Logical
checks
 Completeness
checks
 Reports
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The Solution: SC80HC + FC Census
FC Census
recognition
DevInfo
Data +
Images
Work
Data
Storage & DB
Preparation:
cut & jogg
CSPro
Batch
Header
Archive
Paper
Archive
Data
Storage & DB
Redatam
Form
x
TAPE
Editing
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Local reports
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Data capture
 Data
Processing Centres in different locations
 Peak
period 3 shifts, average 1-2 shifts
 Local
operators trained by our supervisors
 Supervisors
 Central
support from Lab
 Training
 Help
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local
& documentation realised in advance
to design the documents
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Thank you for your attention
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