1. Introduction to Optical Character
Recognition (OCR)

2. Summary
 Overview of OCR
 System Requirements
 Advantages and Disadvantages
 Operation and Management
 Questionnaire Design and Preparation
 OCR Field Operation
 OCR Country Outlook

3. OCR (Optical Character Recognition)
 Function & Features of OCR/ICR
 ICR, OCR and OMR Compared
 Optical Mark Reader (OMR)
 OCR/ ICR

4. OCR (Optical Character Recognition)
 Also referred to as Optical Character Reader
 “…a system that provides a full alphanumeric
recognition of printed or handwritten characters at
electronic speed by simply scanning the form.”(UNESCAP, Pop-
IT project, 1997-2001)
 Intelligent Character Recognition (ICR) is used to
describe the process of interpreting image data, in
particular alphanumeric text.
 Sometimes OCR is known as ICR

5. Functions & Features of OCR
 Forms can be scanned through a scanner and then the
recognition engine of the OCR system interpret the images
and turn images of handwritten or printed characters into
ASCII data (machine-readable characters).
 The technology provides a complete form processing and
documents capture solution.
 Allows an open, scaleable and workflow.
 Includes forms definition, scanning, image
 pre-processing, and recognition capabilities.

6. ICR,OCR and OMR Differences
 ICR and OCR are recognition engines used with
imaging;
 OMR is a data collection technology that does
not require a recognition engine.
 OMR cannot recognize hand-printed or
machine-printed characters.

7. Optical Mark Reader (OMR)
 Forms
 An OMR works with a specialized document and contains
timing tracks along one edge of the form to indicate scanner
where to read for marks which look like black boxes on the
top or bottom of a form.
 The cut of the form is very precise and the bubbles on a form
must be located in the same location on every form.
 Storage
 With OMR, the image of a document is not scanned and
stored.
 Accuracy
 OMR is simpler than OCR.
 designed properly, OMR has more accuracy than OCR.

8. OCR/ ICR
 Forms
 OCR/ ICR is more flexible since no timing tracks or block
like form IDs required.
 The image can float on a page.
 ICR/ OCR technology uses registration mark on the four-
corners of a document, in the recognition of an image.
Respondents place one character per box on this form.
 The use of drop color reduces the size of the scanner’s
output and enhances the accuracy.
 Storage/ retrieval
 If the document needs to be electronically stored and
maintained, then OCR/ ICR is needed.
 OCR/ICR technologies, images can be scanned, indexed,
and written to optical media.

9. OMR-OCR/ICR Compared

10. System Requirements
 Minimum capacity PC Requirements:
 Processor: Pentium 200 MHz RAM: 32 MB Disk: 4 GB
 Form modules are designed to operate in a batch
processing;
 Run under LAN and PC based platforms and take full
advantage of the graphical user interface and 32 bit
processing power available with most Windows
versions.
 Software:
 OCR with ICR capability software
 Questionnaire Design Software

11. System Requirements (cont.)
 Scanner
 OCR scanners with minimum capacity:
 Duplex scanning
 Speed: 60 sheets/ min
 Automatic Document Feeder (ADF):
Scanning can take a significant amount,
and the system lets user scan up without
doing the OCR.

12. Advantages and Disadvantages
 Advantages of Using Images Rather Than Paper
 Quicker processing; no moving or storage of questionnaires near
operators
 Savings in costs and efficiencies by not having the paper
questionnaires
 Scanning and recognition allowed efficient management and planning
for the rest of the processing workload
 Reduced long term storage requirements, questionnaires could be
destroyed after the initial scanning, recognition and repair
 Quick retrieval for editing and reprocessing
 Minimizes errors associated with physical handling of the
questionnaires

13. Advantages and Disadvantages
 Disadvantages of Using Images Rather Than Paper
 Accuracy
 While OCR technology can be effective in
converting handwritten or typed characters, it
does not give as high accuracy as of OMR for
reading data, where users are actually marking
forms
 Additional workload to data collectors OCR has
severe limitations when it comes to human
handwriting
 Characters must be hand-printed with separate
characters in boxes

14. Operation and Management
 OCR Process Stages
 Document Scanning process
 Scanning speed will be determined by the quality of the
scanner machines, the size of non-drop out color. Paper
quality, cleanness, weights.
 Recognizing process
 The recognizing process is to interpret images. The right
memory (dictionary) and the configuration threshold will
determine the accuracy of interpretation of the ICR.
 Verifying Process
 To compare the value of the interpreted image with the real
image of the form.
 Processing can be in geographic order or in random order.

15. Operation and Management (cont.)
 Image Manipulation
 Electronic questionnaires can be sent to specialist operators then
back to the original operator if necessary
 Same questionnaire can be worked on simultaneously by two or
more persons
 Electronic questionnaires are readily available for post census
analysis (easier access to questionnaires)
 Parts of various questionnaires on screen at once for inter record
editing
 Able to view the relevant field book entry on screen in conjunction
with questionnaires which is helpful for coding and editing

16. Operation and Management (cont.)
 Coding Assistance
 The problems are simpler for the operator to identify
 Can use images of questions that will not be captured (scanned but
not recognized) to help the coding process. ex, light pencil.
 Operator can magnify images to read characters not discernible to
the naked eye
 Appropriate software ensures that the data is validated as the
forms are read.
 Checks to ensure selections on a form are filled in.
 Possible to distinguish between intended marks and marks that
have been erased.

17. Operation and Management (cont.)
 OMR Scanner Speed
 Factors
 Skew: Each document is moved from an automatic
feeder into ascanner and angle of skew is
sometimes introduced.
 De-skew: Analyze the image bit- map, calculates
and returns the angle of skew up to +/-25.
Example. De-skew often refer to %, which is the
pixel shift. 10% is a 20-pixel shift in a line of 200
pixels or one tenth of an inch in an inch long line.

18. Operation and Management (cont.)
 Landscape Detection and Auto Rotation:
 landscape detection will automatically detect
and rotate appropriate images 90 degrees.
 White Page Detection:
 Normally, a double-sided scanner creates two
images per scanners page.
 However, if the back or front page is blank,
there is no need to store this image.
 White page detection
 Allows the user to avoid storing blank page.

19. Operation and Management (cont.)
 Other Factors
 Automatic Image Registration
 De-Speckle and Shade Removal
 Character Enhancer
 Cost Savings
 Automatic processes to improve recognition
rates
 Voting techniques, Multiple engines, Learning

20. Questionnaire Design and
Preparation
 Drop Out Color
 Usually red- the color facility in OCR system that
allows the system to pick up only the meaningful
information from an OCR form.
 The system doesn't need to know the values
including tick boxes written in the drop out color.
 The OCR system only needs to see the black parts,
and compares them to specifications to see parts
that are filled or written.
 Characters or Marks
 Considering the speed of the data capture process
and to reduce rates, it is advisable to use marks or
“ticks” as much as possible

21. Questionnaire Design and
Preparation (cont.)
 How to Obtain Good Results of Scanning
 Select adequate paper quality; Reliable printing press.
 Appropriate ink, considering drop out color, for the questionnaires paper
heavier than 80 grams per square meter can help avoid paper crashes or
over read the other side of a single page.
 Form Design Advise
 Number items to be included in a form; Design size of boxes for each
character answer carefully.
 Define drop out color properly; use registration marks.
 Pre-print the codes near the place where the box for ticks are located
 Maintain consistent pattern in which the information to be collected will be
located.
 Do not disturb the visibility of the ticks and marks with titles, labels or
instructions.
 Avoid putting "answers" of one field to another page of the questions;
Avoid using open ended questions

22. OCR Field Operation
 Training for Collection and Processing Staff
 Basic software, scanner operations, including
installation and troubleshooting.
 Applications with emphasis on the development of
custom applications including: configuring
nonstandard forms
 Pre-marking of forms, use of overprinting customize
forms
 Processing of surveys
 Crating custom outputs file formats

23. OCR Field Operation (cont.)
 Reasons of Error- Reading of OCR
 Bad condition of the form because of dirt, folded, crumple, etc.
 Forms fed into OCR scanner are not straight (at an angle); Incompletely filled
 Reduce Error-Reading of OCR
 Checking the questionnaires for completeness and consistencies; Preparation of own memory (dictionary);
Defining permissible margins of OCR reading errors
 Particular Care in Writing Numbers or Alphabetic
 One box contains only one character; Characters should not extend outside designated boxes; Unnecessary
lines of characters such as points, decorative strokes, hooks, etc. are prohibited. Strokes should not be ended
with flourishes or extensions.
 All lines should be connected without breaks; All lines or dots should be pressed with the same pressure.
 Value Checking Steps: Verify that the information captured by OMR is the same with the questionnaire
 Control for Blank: If the information is blank, what type of control must be taken.
 Control steps should be taken if the information image is partial or no information to assure the quality of
generated files.
 Missing Questionnaire; Make sure that the entire questionnaires are scanned
 completely, no missing and no duplication as well.
 Therefore control procedures including to produce control tables to compare with manual work.

24. OCR Country Outlook
 Countries using optical mark recognition
 (Greece)
 Countries using optical character recognition
 (Croatia- in use for the next census round)
 (Japan-out-sources entire process and in use for the next
census round)
 Countries using both
 Belgium
 Countries planning to use OCR
 Tajikistan
 (Tonga) looking to introduce and use OCR for our next Census

25. OCR Country Outlook
 Common device/scanner and software used by
NSOs
 (Croatia) KODAK DS3520 bitonal scanners, IBM IFP
(intelligent Forms Processing)
 (Greece) OMR- devices/scanners were ‘’axm
990/995’’ with FORM/ AXF/ ADELE+ software
 (New Zealand) Kodak scanners i830 and i7620 -
scanning and raw data capture process (recognition
aspect) were outsourced.- For the next census -end
scanning and data capture process will more than
likely be outsourced but it really is a variation to a
current supplier agreement.
 (Belgium) AGFA (high resolution) scanner

26. OCR in Use
 Editing method used for the census
 (Japan) cold-deck method, hot-deck method, etc.
 (Croatia) in house developed – logical checking and automatic
and manual correcting
 (Greece) via PC- editor (officer of N.S.S.G.) confirms or rejects
a non-accurate value or inputs a missing one.
 (New Zealand) mixture of micro and macro editing practices.
Individual responses may have range or validity edits, inter-
field edits and also inter-form edits (within a household).
Macro editing is particularly used during the data evaluation
process and data may be reprocessed as a result of this

27. OCR Country Outlook
 Common commercial or free software used in
OCR
 (Croatia) Use ACTR (automated coding by text
recognition) for coding -software developed by
Statistics Canada.
 (Greece) Commercial software, after an open
bidding, according to the budgetary plan of the
population census
 (New Zealand) IBM Intelligent Forms Processing
(IFP) system through an established user agreement.
 (Belgium) IRIS (Image Recognition Integrated
Systems)

28. OCR Country Outlook
Concerns/issues with the use of optical
character recognition for data capture
for the census?
 (Japan) Speed of data capture and recognition,
recognition accuracy of Japanese characters, etc.
 (Greece) OMR -related to the optical recognition of
numbers, the rapidity of optical recognition itself
and the electronic storage of the questionnaires.
 (Tajikistan) Getting equipment and training.
 (Samoa) Not enough financial support and
technical human resources.

29. THANK YOU!