问题
I am developing an OCR to detect credit card.
After scanning the image I get a list of words with it´s positions. Any tips/suggestions about the best approach to detect which words correspond to each field of credit card (number, date, name)?
For example:
position = 96.00 491.00
text = CARDHOLDER
Thanks in advance
回答1:
Your first problem is that most OCRs are not optimised for small amounts of text that take up most of the "page" (or card image, in your case) in spatially separated chunks. They expect lines, or pages of text from a scanned book or a newspaper. So straight away they're not likely to do that well at analysing the image.
Because the font is fairly uniform they'll likely recognise the characters well, but the layout will confuse the page segmentation algorithm and so the text you get out might not be in the right order. For example, the "1234" of the card number and the smaller "1234" below it constitute a single column of text, likewise the second two sets of four numbers and the expiration date.
For specialized cases where you know the layout in advance you really want to develop your own page segmentation algorithm to break up the image into zones, e.g. card number, card holder name, start and expiration dates. This shouldn't be too hard because I think the location of these components are standardised on credit cards. Assuming good preprocessing and binarization you could basically do a horizontal histogram and split the image at the troughs.
Then extract each zone as a separate image containing just one line of text and feed it to the OCR.
Alternately (the quick and dirty approach)
- Instruct the OCR that what you want to recognise consists of a single column (i.e. prevent it from trying to figure out the page layout itself). You can do this with Tesseract using the
-psm(page segmentation mode) parameter set to, probably, 6 (but try and see what gives you the best results) - Make Tesseract output hOCR format, which you can set in the configfile. hOCR format includes the bounding boxes of the lines that get output relative to the whole image.
- write an algorithm that compares the bounding boxes in the hOCR to where you know each card component should be (looking for some percentage of overlap, it won't match exactly for obvious reasons.)
回答2:
In addition to the good tips provided by Mikesname, you can greatly improve the recognition result regardless of which OCR engine you use if you use image processing to convert the image to bitonal (pure black and white), such as the attached copy of your image.
来源：https://stackoverflow.com/questions/27046590/ocr-match-frame%c2%b4s-position-to-field-in-credit-card