How to improve Text recognition usingTesseract OCR.? - ios

Question

I had implemented tesseract ocr for text recognition in IOS.I had preprocessed the input image and give into Tesseract method.It gives poor recognition result.
Steps:
1.Erode function
2.Dilate function
3.Bitwise_not function
Mat MCRregion;
cv::dilate ( MCRregion, MCRregion, 24);
cv::erode ( MCRregion, MCRregion, 24);
cv::bitwise_not(MCRregion, MCRregion);
UIImage * croppedMCRregion = [self UIImageFromCVMat:MCRregion];
Tesseract* tesseract = [[Tesseract alloc] initWithDataPath:#"tessdata" language:#"eng"];
[tesseract setVariableValue:#"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz.>,'`;-:</" forKey:#"tessedit_char_whitelist"];
[tesseract setImage:[self UIImageFromCVMat:MCRregion]];
// [tesseract setImage:image];
[tesseract recognize];
NSLog(#"%#", [tesseract recognizedText]);
Input Image:
Image Link
1.How to Improve text recognition rate using Tesseract ?
2.Is any other pre processing steps applied in Tesseract.?
3.Is dewarp text Done in Tesseract OCR.?

Answer 1

Tesseract is a highly configurable piece of software -- though its configurations are poorly documented (unless you want to dig deep in the 150K lines of code). A good comprehensive list is present here http://www.sk-spell.sk.cx/tesseract-ocr-parameters-in-302-version.
Also look at
https://code.google.com/p/tesseract-ocr/wiki/ControlParams and https://code.google.com/p/tesseract-ocr/wiki/ImproveQuality
You can improve the quality tremendously if you feed more info about the data you're OCR'ing.
e.g. in case the images are all National IDs or Passports which follow certain standard MRZ formats, you can configure tesseract to use that info.
For the image you attach (an MRZ), i got the following result,
IDFRADOUEL<<<<<<<<<<<<<<<<<<<<9320
05O693202O438CHRISTIANE<<N1Z90620<3
by using the following config
# disable dict, freq tables etc which would distract OCR'ing an MRZ
load_system_dawg F
load_freq_dawg F
load_unambig_dawg F
load_punc_dawg F
load_number_dawg F
load_fixed_length_dawgs F
load_bigram_dawg F
wordrec_enable_assoc F
# mrz allows only these chars
tessedit_char_whitelist 0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ<
Also see that your installation is trained for the fonts to get more accurate results. In your case it seems it is OCR-B font.

Answer 2

It is not necessary to go through the tedious task of retraining Tesseract. Yes, you will get much better results but in some cases you can get pretty far with the ENG training set.
You can improve your results by paying attention to the following things:
Use a binary image as input and make sure you have black text on a white background
By default Tesseract will try to make words out of things that have no spacing. Try to segment each character seperately and place them in a new image with lots of spacing. Especially if you have combinations of letters and numbers Tesseract will "correct" this to match the surrounding characters.
Try to segment different parts of your image with a whitelist for the characters you know should be in there. If your only looking for digits in the first part then use a seperate instance of Tesseract to detect these numbers with a number only whitelist.
If you use the same object multiple times without resetting it Tesseract seems to have a memory. That means that you can get a different result each time you perform OCR. You can reset Tesseract to counter this or just create a new object.
Last but not least, use the resultIterator to go through the boxes that Tesseract can give as a result. You can check the size and confidence of each character and filter accordingly.

Answer 3

Based on my experience:
1.How to Improve text recognition rate using Tesseract ?
Firstly, preprocessing. Ensure that the input image is binary image with a good threshold. OpenCV has a good set of functions to apply threshold algorithms such as the Otsu algorithm as well as contour detection to help with warping and rotation.
You can also use contour detection in OpenCV to distinguish between lines of text.
Some filtering would also remove noise which often confuse tesseract and increase processing time.
Set up proper configurations for tesseract (e.g. eng.config). Full list of configs here (http://www.sk-spell.sk.cx/tesseract-ocr-parameters-in-302-version). Some examples include blacklists, whitelists, chopping, etc...
Use proper flags. E.g. -psm 6 if you are doing blocks of text rather than lines
Having trained my own language data... I would say do so only if you have lots of time and resources. Or if your font is very peculiar (e.g. dot matrix).
More recent versions of Tesseract (closer to 3.0) allow for multiple language files to be used on the same pass (-l one+two). This means you can have one specially trained for text and another for numbers. In our case, it seemed to work well.
Postprocessing of tesseract results was particularly important for us too. String replacements of typical mis-recognitions and what not.
2.Is any other pre processing steps applied in Tesseract.?
Tesseract uses leptonica library for preprocessing.
3.Is dewarp text Done in Tesseract OCR.?
I am inclined to think yes. Considering that warping functions are part of leptonica.