Thanks I'll give those a shot, do you know of any way to use hOCR to solve this problem?
On Tuesday, February 4, 2014 3:46:56 AM UTC-7, Aleksander Grzyb wrote: > > Generally finding text on color images is very hard, but in your case, > your business card image is in binary, so I have two possible solutions in > my mind: > > 1. You can use OpenCV function findContours() which will detect > contours of letters, then you can filter that contours (using hierarchy). > After that you put bounding rect on that filtered contours. By now you > have > coordinates of letters and shapes on you business card. Based on that > information you can group the bounding rects to contain your blocks of > texts. > 2. On your binary image there are only white and black colors. So you > have 2 cases, where letters and shapes are white and background black or > where letters and shapes are black and background white. You can use > histogram to detect the color of letters. Next you can use histogram on > every row of image and on every column of image and see where are the > blocks of text. > > On Monday, February 3, 2014 11:41:30 PM UTC+1, Nick Porter wrote: >> >> Thanks for the reply Aleksander. These improved the accuracy of my scans, >> however it does not provide a sloution to detecting paragraphs and blocks >> of text. Any idea how to do this? >> >> On Monday, February 3, 2014 2:16:15 AM UTC-7, Aleksander Grzyb wrote: >>> >>> To improve results you should try to: >>> >>> 1. Convert image to binary image. >>> 2. Crop the image to get rid off the surroundings. >>> 3. Detect skew of image and do some perspective transform. >>> >>> I recommend to use OpenCV to do this operations. There is a pod for >>> OpenCV: >>> >>> https://github.com/Fl0p/OpenCV-iOS >>> >>> Here are some links that should help you do the image processing part: >>> >>> >>> http://stackoverflow.com/questions/8667818/opencv-c-obj-c-detecting-a-sheet-of-paper-square-detection >>> >>> http://stackoverflow.com/questions/6555629/algorithm-to-detect-corners-of-paper-sheet-in-photo >>> >>> http://stackoverflow.com/questions/8637867/skew-detection-and-reduction-in-opencv >>> >>> http://stackoverflow.com/questions/7838487/executing-cvwarpperspective-for-a-fake-deskewing-on-a-set-of-cvpoint >>> >>> W dniu piątek, 31 stycznia 2014 20:44:55 UTC+1 użytkownik Nick Porter >>> napisał: >>>> >>>> I am trying to scan a business card using tesseract OCR, all I am doing >>>> is sending the image in with no per-prossesing, heres the code I am using. >>>> >>>> Tesseract* tesseract = [[Tesseract alloc] initWithLanguage:@"eng+ita"]; >>>> tesseract.delegate = self; [tesseract >>>> setVariableValue:@"0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ@.-()" >>>> forKey:@"tessedit_char_whitelist"]; [tesseract setImage:[UIImage >>>> imageNamed:@"card.jpg"]]; //image to check [tesseract recognize]; >>>> NSLog(@"Here is the text %@", [tesseract recognizedText]); >>>> >>>> Picture of card <http://imgur.com/nQPG6iq> >>>> >>>> This is the output <http://imgur.com/poikzBn> >>>> >>>> As you can see the accuracy is not 100%, which is not what I am >>>> concerned about I figure I can fix that with some simple per-processing. >>>> However if you notice it mixes the two text blocks at the bottom, which >>>> splits up the address, and possibly other information on other cards. >>>> >>>> How can I possibly use Leptonica(or something else) to group the text >>>> somehow? Possibly send regions of text on the image individually to >>>> tesseract to scan? I've been stuck on this problem for a while any >>>> possible >>>> solutions are welcome! >>>> >>> -- -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/tesseract-ocr?hl=en --- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/groups/opt_out.
