OCR Technology

The most commonly accepted OCR accuracy measurement is represented by the percentage of characters correctly read on a given page of text, and systems vary widely, achieving 95 to 99 percent accuracy. But accuracy rates at anything below 100 percent can translate into huge productivity losses. An entire application or verification process could be compromised if even 5 percent of the data is either entered incorrectly or misread. Therefore, OCR systems must have the ability to ‘proofread’ results, mark characters the system does not recognize, and send rejected text to human operators for manual processing. Needless to say, such human intervention increases costs and delays.

Best performing OCR

Optical Character Recognition (OCR) – used extensively throughout business and government – examines scanned bitmap images of machine-printed text and translates the characters into ASCII text files that can be edited. For instance, paper checks contain number series written in machine print designed to minimize recognition errors. These codes contain bank routing numbers, the holder’s account numbers and other information required to process paper transactions. Machine print conversion is largely a solved problem in this application, as OCR software was included in the first commercial systems that automated machine print text recognition.

Principles of OCR Technology

Optical Character Recognition (OCR) systems recognize only machine print. Using pattern-matching technology, OCR translates the shapes and patterns of machine-made characters into corresponding computer codes. Though most advanced systems are able to recognize multiple fonts, they can process only standard fonts such as Times Roman and Arial. Once all characters in a given word are recognized, the word is compared against a vocabulary of potential answers for the final result.

Character recognition then segments lines of text or words into separate characters that are recognized by the makeup of their component shapes. Machine-printed letters are evenly spaced across, and up-and-down, a given page, allowing the OCR system to read the text one character at a time. Segmentation into single characters represents a critical recognition failure point for forms processing organizations, because OCR recognition technology requires high-quality images with excellent contrast, character and clarity. Any text that is less than perfect will cause even the most sophisticated OCR systems to return significant reductions in accuracy when processing degraded images. For example, when characters break apart due to poor image quality, or if multiple characters merge due to blurred or dark backgrounds between them, recognition accuracy may be reduced by as much as 20 percent.

ICR Technology - Recognizes machine print and handprint. However, it rejects any letter shapes formed as cursive script.



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