Exceptions in OCR and data capture are usually thought of as mis-recognized characters only, but in reality there are several other types of exceptions that exist. One of those is called “high confidence blanks”. A “high confidence blank” in OCR or data capture is where the software looked in a particular region for a character but no text was identified or read. In data capture “high confidence blanks” usually occur for entire fields or just the first character; in full-page OCR they are less common but can occur sporadically throughout the text of the document or the entire text. This type of exception is elusive and hard to detect. Obviously if entire fields and text is missed where you expect there to be text it is easy to spot, but for the one-off missing characters it’s tough. With full-page OCR detection is done with spell-check. Missing characters in a word will surely flag the word as being misspelled. In data capture it’s much more tricky and the best thing to do is to take certain steps to avoid “high confidence blanks”.
1.)The first thing you can do to avoid “high confidence blanks” in data capture is to NOT over use image clean-up. If characters are regenerated or cleaned too much they look to the OCR engine to be just a graphic not a typographic character and thus avoided.
2.)Second if you have control of the form design make sure text is not printed close to lines, this is one of the biggest generators of “high confidence blanks”
3.)If text is close to lines then you should be able to establish a rule in the software indicating for example that if the first character in a field is more then x pixels away from the border then most likely a character(s) was missed.
4.)If at all possible use dictionaries and data types that state the structure of the information that should be present in a field. If a character is missing this data type will likely be broken.
This type of exception is one that leads to hidden downstream problems when organizations don’t realize that it might happen. Being aware and taking the proper steps to avoid “high confidence blanks” is the solution.
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.