The trend of companies promoting OCR voting has become less common, but you will still occasionally find products that promote their accuracy by saying they don’t just use one engine they use many and vote them together. The presumption of this approach is that of course they are more accurate then single engine solutions. This would seem to be the case, but it’s not that easy.
All the OCR engines have a system of voting internally already. This is how OCR technologies have made their advances throughout the years. They take algorithms that are expert in one particular way to interpret text, such as trigrams, words, fonts, etc. and vote their character guesses against each other for the final guess. This works great. This is very different from the voting that is often promoted of taking several engines and voting their result together. When you take two separate OCR engines and vote them together it would seem you are getting the best of what’s available, but there is one major problem. Voting requires that each engine guess the same way, and this is not the case. For example Engine A might report a confidence on the letter “c” at 98% that it’s actually an “e” while Engine B might report with a 78% confidence that I is a “c”. When you vote these two, Engine A will win even though it’s wrong. This is typically how it goes, one engine in a voting scenario will win most of the time right or wrong, just because of how it reports it’s confidence levels.
This blog is not in combat with voting. Voting is a great tool, it’s used internally in the engines, and it can be used externally as well. How? Vote Engine A settings A against Engine A settings B. The same engine voted against itself just will different settings. This is a tremendous tool especially when dealing with varied documents, or highly degraded documents. By doing so you are comparing apples-to-apples confidence levels not apples-to-elephants.
So next time you are turned on by voting, take a second look and see if it’s a marketed or real value.
Chris Riley – Sr. Solutions Architect
Ilya Evdokimov is a long-term practitioner and expert in leading Optical Character Recognition (OCR), Data Capture and Document Processing techniques, technologies and solutions. With over 15 years of experience spanning enterprise software implementations, mobile applications development, cloud-based systems integration and desktop-level automation, Ilya Evdokimov uses through industry knowledge and experience to achieve high efficiency and workflow optimization in most challenging paper-dependent and digital image capture environments.