Optical mark recognition

Overview

Optical mark recognition is the process of capturing data by contrasting reflectivity at predetermined positions on a page. By shining a beam of light onto the document the scanner is able to detect a marked area because it is more reflective than an unmarked surface.

It is generally distinguished from optical character recognition by the fact that a recognition engine is not required. That is, the marks are constructed in such a way that there is little chance of not reading the marks correctly. This requires the image to have high contrast and an easily-recognizable or irrelevant shape.

One of the most familiar applications of optical mark recognition is the use of #2 pencil bubble optical answer sheets in multiple choice question examinations. Students mark their answers, or other information, by darkening circles marked on a pre-printed sheet. Afterwards the sheet is automatically graded by a scanning machine. Most people know this by the eponym Scantron after the company that is best known for this. In most European countries, a horizontal or vertical 'tick' in a rectangular 'lozenge' is the most commonly used type of OMR form, the most familiar application being the UK National lottery form.

Other examples of OMR are the MICR recognition of the numbers on the bottom of checks, scannable bar codes.

Recent improvements in OMR have led to various kinds of two dimensional bar codes called matrix codes. For example, United Parcel Service (UPS) now prints a two dimensional bar code on every package. The code is stored in a grid of black-and-white hexagons surrounding a bullseye-shaped finder pattern. These images include error-checking data, allowing for extremely accurate scanning even when the pattern is damaged.

Most of today's OMR applications work from mechanically generated images like bar codes. A smaller but still significant number of applications involve people filling in specialized forms. These forms are optimized for computer scanning, with careful registration in the printing, and careful design so that ambiguity is reduced to the minimum possible. Due to its extremely low error rate, low cost and ease-of-use, OMR is a popular method of tallying votes.

History

Optical mark recognition (OMR) is the scanning of paper to detect the presence or absence of a mark in a predetermined position (Haag, 2006). Optical mark recognition has evolved from several other technologies. In the early 1800’s and 1900’s patents were given for machines that would aid the blind (Bookrags, n.d.). One such machine, the optitone, converted s OMR is now used as an input device for data entry. Two early forms of OMR are paper tape and punch cards which use actual holes punched into the medium instead of pencil filled circles on the medium. Paper tape was used as early as 1857 as an input device for telegraph (Yurcik, n.d). Punch cards were created in 1890 and were used as input devices for computers. The use of punch cards declined greatly in the early 1970’s with the introduction of personal computers (Palmer, 1989). With modern OMR, where the presence of a pencil filled in bubble is recognized, the recognition is done via an optical scanner. The first OMR scanners however read the mark by conducting electricity between the lead in the pencil and minute electrodes in the machine (Lopresti, 1996).

OMR has been used in many situations as mentioned below. The use of OMR in inventory systems was a transition between punch cards and bar codes and is not used as much for this purpose (Palmer, 1989). OMR is still used extensively for surveys and testing though.

General

The use of OMR is not only limited to schools or data collection agencies; many businesses and health care agencies use OMR to streamline their data input processes and reduce input error (“Who uses Remark Office OMR”, n.d).

Applications

There are many other applications for OMR, for example:

In the process of institutional research
Community surveys
Consumer surveys
Tests/assessments
Evaluations/Feedback
Data compilation
Product evaluation
Time sheets/Inventory counts
Membership subscription forms
Lotteries/Voting (“Who uses Remark Office OMR”, n.d.)
Geocoding (e.g. postal codes)

Fields

OMR has different fields to provide the format the questioner desires. These fields include:

Multiple, where there are several options but only one is chosen, ABCDE, 12345, completely disagree, disagree, indifferent, agree, completely agree, etc.
Grid, the bubbles or lines are set up in a grid format for the user to fill in a phone number, name, ID number and so on.
Add, total the answers to a single value
Boolean, answering yes or no to all that apply
Binary, answering yes or no to only one (Martin, n.d.).

Capabilities/requirements

In the past and presently, some OMR systems require special paper, special ink and a special input reader (Bergeron, 1998). This restricts the types of questions that can be asked and does not allow for much variability when the form is being inputted. Progress in OMR now allows users to create and print their own forms and use a scanner (preferably with a document feeder) to read the information (Bergeron, 1998). The user is able to arrange questions in a format that suits their needs while still being able to easily input the data (LoPresti, 1996). OMR systems approach one hundred percent accuracy and only take .005 seconds on average to recognize marks (Bergeron, 1998; “LeadTools Optical Mark Recognition”, 2006). Users can use squares, circles, ellipses and hexagons for the mark zone. The software can then be set to recognize filled in bubbles, x’s or check marks (“Intelligence in Document Imaging”, n.d.).

OMR can also be used for personal use. There are all-in-one printers in the market that will print the photos the user selects by filling in the bubbles for size and paper selection on an index sheet that has been printed. Once the sheet has been filled in, the individual places the sheet on the scanner to be scanned and the printer will print the photos according to the marks that were indicated (M. Meek, personal communication, Feb 11, 2006).

Disadvantages

There are also some disadvantages, limitations to OMR. If the user wants to gather large amounts of text then OMR complicates the data collection (Green, 2000), there is also the possibility of missing data in the scanning process, incorrectly or unnumbered pages can lead to them being scanned in the wrong order. Also, unless safeguards are in place, a page could be rescanned providing duplicate data and skewing the data (Bergeron, 1998).

For the most part OMR provides a fast, accurate way to collect and input data, however it is not suited for everyone’s needs.

References

Bergeron, Bryan P. (1998, August). Optical mark recognition. Postgraduate Medicine online. Retrieved June 7, 2006 from http://www.postgradmed.com/issues/1998/08_98/dd_aug.htm

Bookrags. (n.d.) Optical Character Recognition. Retrieved June 11, 2006, from http://www.bookrags.com/sciences/computerscience/optical-character-recognition-csci-02.html

Green, Phil (2000). Optical Scanning Systems. Retrieved June 9, 2006 from http://www.aceproject.org/main/english/et/et72.htm

Haag, S., Cummings, M., McCubbrey, D., Pinsonnault, A., Donovan, R. (2006). Management Information Systems for the Information Age (3rd ed.).Canada: McGraw-Hill Ryerson.

Lead Technologies. (2006). LeadTools Optical Mark Recognition. Retrieved June 11, 2006 from http://www.leadtools.com/SDK/Document/Document-Addon-OMR.htm

LoPresti, Frank and Naphtali, Zvia Segal. (1996) Statisticians’ Lib: Using Scanners and OMR Software for Affordable Data Input. Statistics and the Social Sciences. Retrieved June 7, 2006 from http://www.nyu.edu/its/pubs/connect/archives/96fall/loprestistats.html

Martin, Ann. (n.d.) Data Collection on the Cheap: A System for Small budgets and Small Organizations. Retrieved June 9, 2006 from http://www.lco.edu/facstaff/asmnt/Data%20Collection%20on%20the%20Cheap%20Format.ppt#1

OMR Solutions. (n.d.) Who uses Remark Office OMR Retrieved June 9, 2006 from http://www.omrsolutions.com/principia/whousesremark.php

Palmer, Roger C. (1989, Sept) The Basics of Automatic Identification [Electronic version]. Canadian Datasystems, 21 (9), 30-33

Tech Vision. (n.d.) Intelligence in Document Imaging. Retrieved June 7, 2006 from http://www.tkvision.com/tech/technology.htm

Yurcik, William J. (n.d.) Optical Character Recognition. Retrieved June 11, 2006, from http://www.bookrags.com/sciences/computerscience/input-devices-csci-02.html

Software for OMR

Official Website of ABBYY OCR/OMR/ICR Recognition Software
Official Website of Remark Products Group OMR Software

Gourt Home::Gourt :: Optical mark recognition