An important part of data capture, OMR, or optical mark recognition (sometimes called optical mark reading) is the process of digitally capturing human marked data from paper documents. It works by scanning a printed form, reading the predefined positions and recording where marks are made on the form. A common OMR application is the use of “bubble sheets” for multiple-choice tests and state administrated standardized tests used in schools and colleges. The student indicates the answer on the test by filling in the corresponding bubble, and the form is fed through an OMR.
In businesses, OMR is very useful for analyzing hand-filled data from surveys, tests, attendance sheets, checklists, questionnaires, reply cards, time sheets, inventory counts, and more. It is also commonly used in the health care industry, in loan, banking and insurance applications, in institutional research, in analyzing consumer, product and employee feedback, to sort mail by postal code, and to efficiently tally political voter ballots. As long as the image marked has high contrast and is an easily-recognizable or irrelevant shape, OMR technology processes information very quickly and very accurately, with an error rate of less than one percent.
OMR has different fields to provide the format the questioner desires for optimal results. They include multiple, where there are several options, usually of letters or numbers, but only one is chosen; grids, the bubbles or lines are set up in a grid format for the user to fill in their name and other identifying information; add, total the answers to a single value; Boolean, answering yes or no to all that apply; and binary, answering yes or no to only one. Such forms have been carefully designed so that ambiguity is reduced to a minimum.
Many traditional OMR devices work with a dedicated scanner device that shines a beam of light onto the form paper. The contrasting reflectivity at predetermined positions on a page is then used to detect the marked areas because they reflect less light than the blank areas of the paper. OMR is generally differentiated from optical character recognition (OCR) by the fact that a complicated pattern recognition engine is not required. This means the marks are constructed in such a way that there is little chance of not reading the marks correctly, as long as the marks are clearly distinguishable. Optical Mark Recognition is a great way to quickly, accurately, and cost-effectively process human marked documents necessary for corporate information.