Just this morning, I was reminded of why market education is so important. I received an email in the morning from a customer who has been exposed to data capture technology for many years. This customer owns a semi-structured data capture solution that is capable of locating fields on forms that changes from variation to variation. In an attempt to help my understanding, we started a conversation about their expectations. Very wisely, the customer broke down their expectations into three categories: OCR accuracy ( field level ), field location accuracy, and amount of time to process per document. This is a step more advanced than a typical user who will clump all of this into one category. In addition to this, there should be a minimum template matching accuracy. In any case, they expect an OCR accuracy of 90%, which is reasonable considering the document they are working with are pixel perfect. They expect a 20 page document to be processed in 4 minuets which is also reasonable and right on the line. Finally, they expect field location to be 100%, RED FLAG!
This is not the first time that there is an assumption that you can locate fields on a semi-structured form with 100% accuracy, 100% of the time. To my dismay, as people seem to be learning more about the technology, this is the next class of common fallacy. And because the organization did not specify template matching accuracy, it means they must also assume templates match 100% of the time to get 100% field location accuracy. Trouble.
It’s clear as to why 100% field accuracy is important for them. That is because, basic QA processes are capable of only checking recognition results ( OCR Accuracy ), and not locations of fields. Instead of modifying QA processes, an organization’s first thought was how to eliminate the problems that QA might face. 100% accuracy is not possible no matter what is done, including straight text parsing. In this case, the reason it’s not possible is that even in a pixel perfect document, there are situations where a field might be located partially, located in excess, or not located at all. The scenario that most often occurs in pixel perfect documents is that text may sometimes be seen as a graphic because it’s so clean, and text that is too close to lines are ignored. So typically in these types of documents, any field error is usually a field located partial error. Most QA systems can be setup such that rules are applied to check data structure of fields, and if the data contained in them is faulty, an operator can check the field and expand it if necessary. But this is only possible if the QA system is tied with data capture.
After further conversation, it became clear that the data capture solution is being forced to fit in a QA model. There are various reason as to why this may happen: license cost, pre-existing QA, or miss-understanding of QA possibilities. This is very common for organizations and very often problematic. Quality assurance is a far more trivial processes to implement than data capture. When it comes to data capture it would be more important to focus on the functionality of the data capture system and develop a QA that makes it’s output most efficient.
Again, a case of expectations and assumptions.
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.