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ID processing includes various techniques for image and data capture from documents like Passports, Driver Licenses, National Cards, government-issued identification cards, military cards, and many other such document types.

OCR (Optical Character Recognition) is a time-proven technology to convert image-based pictures of characters into digital text. It is based on machine vision and patterns recognition much like a human eye, so it does not require any special formatting – a normal human-readable text can be recognized. Performing generic or specialized OCR on different types of IDs can be done successfully, and can produce relatively fast and quite accurate data recognition and data capture results. OCR is prone to errors from heavy backgrounds obscuring and mixing with the text, so frequently a powerful image pre-processing and cleanup techniques are used to improve and prepare images for OCR.

OCR with Generic Text Parsing process is used to extract meaningful information from text returned from OCR. In this case any returned text is programmatically “searched” for expected data elements.
Example:
o Find the text label “DOB”
o Find data type MM/DD/YYYY next to label
o Conclude finding field type DOB
This search can be done without placement information, i.e. on the whole document, which makes this approach format-independent. Redundant search can be used to improve data finding in less-than-perfect scenarios, such as when DOB label could not be recognized because of OCR errors or heavy background, yet the date in format MM/DD/YYYY where the year starts with “19xx” (to filter out other dates such as expiration date or issue date) can still be found successfully.

– OCR with Template-based Data Extraction process is used to extract meaningful information from text returned from OCR using specific knowledge of formatting and positioning of data. In this case any returned text is programmatically matched against a known “template” and then specifically “searched” for expected data elements specific to that template.
Example:
o Find the markings to detect “California Driver License”, i.e. Classify the document and apply appropriate layout template
o Find the text label “DOB” on the 3rd line and left side of ID
o Find data type MM/DD/YYYY next to label1, below another label0, and above another label2
o Conclude finding field type DOB
This search is done with placement information, i.e. in specific areas, which makes this approach more accurate. But this approach requires creation and management of layout templates.

In the below example, even though a template approach was used for California Driver License, it is visible that the captured text has both template-based errors (wrong data was captured) as well as OCR errors (some characters were recognized incorrectly)

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