White Papers

In doing forms data capture, whether with just human data entry keying from paper forms or with Optical Character Recognition (OCR), Optical Mark Recognition (OMR) and Key From Image (KFI), it has been customary to employ manual methods for data quality assurance.  These methods involve a process we refer to as Double Key & Verify (DK&V), wherein one keyer is asked to key a particular data field, and then another keyer is asked to key the same field (preferably without collusion).  If the results from both keyers agree with the sampled field, then the sampled data field is deemed to be correct.  If they do not, then a third party is usually employed to divine the correct answer.  This classic DK&V process is slow and costly; so, in practice, the amount of data sampled for Quality Assurance (QA) purposes is often smaller than desired for useful statistically valid results. 

A major problem with capturing data from forms filled out by respondents is measuring the accuracy and efficiency of the system. This is true whether traditional “heads-down” keying from paper (KFP), “heads-up” keying from image (KFI), and/or handprint Optical Character Recognition (OCR/ICR) is capturing the data.

It is a fundamental fact that in order to improve the accuracy and efficiency of a system, it is necessary to be able to measure these performance factors. Using our new, patent pending Digital Test Deck® technology, you can now assess your forms processing system more easily and accurately than ever before.

Forms processing technology can be a bit complicated, so in this white paper, we attemptto provide a simple explanation of the basics, and how Digital Test Deck ® technology can be used to save you a lot of money while insuring system accuracy.

In helping the U.S. Census Bureau develop and test the data capture systems for the Year 2000 and 2010 Decennial Census, we have developed a unique testing approach for handprint data capture systems. This approach not only tests the inherent quality of the handprint data capture automation and keying, but also monitors the progress in forms design, where the objective is to produce a form that is both "respondent-friendly" and "data capture-friendly". We illustrate this approach to testing with examples from our Census experience, and also from a commercial client study. Using this technology, we produced two test decks containing the same respondent data; one on a “poor” form design, and one on a “good” form design, and actually measured the data capture quality from each design. These results, along with related cost estimates, enable us to estimate the possible Return On Investment (ROI) due to better handprint recognition resulting from an improved forms design.

This is a presentation given at the AIIM 2000 Conference in New York. We explained the recognition problem, data fields vs. characters, soft and hard match rules, testing and results from Census 2000.