I have a question regarding achieving better recognition results with tesseract. I am using tesseract to recognize serial numbers. The serial numbes consist of only one font-type, characters A-Z, 0-9 and occur in different sizes and lengths.
At the moment I am able to recognize about 40% of the serial number images correct. Images are taken via mobile phone camera. Therefore the image quality isn't the best.
Special problem characters are 8/B, 5/6. Since I am recognizing only serial numbers, I am not using any dictionary improvements and every character is recognized independently.
My question is: Does someone has already experience in achieving better recognition results with training tesseract? How many images would be needed to be able to get good results.
For training tesseract should I use printed and afterwards photographed serial numbers, or should I use original digital serial numbers, without printing and photographing?
Maybe somebody has already experience in that kind of area.
Regarding training tesseract: I have already trained tesseract with some images. Therefore I have printed all characters in different sizes, photographed and labeled them correctly. Example training photo of the character 5
Is this a good/bad training example? Since I only want to recognize single characters without any dependency, I though I don't have to use words for training.
Actual I only have trained with 3 of these images for the characters B 8 6 5 which doesn't result in a better recognition in comparison with the original english (eng) tesseract database.
best regards,
Christoph
I am currently working on a Sikuli application using Tesseract to read text (Strings and numbers) from screenshots. I found that the best way to achieve accuracy was to process the screenshot before performing the OCR on it. However, most of the text I am reading is green text-on black background, making this my preferred solution. I used Scalr's method within BufferedImage to increase the size of the image:
BufferedImage bufImg = Scalr.resize(...)
which instantly yielded more accurate results with black text on gray background. I then used BufferedImage's options BufferedImage.TYPE_BYTE_GRAY and BufferedImage.TYPE_BYTE_BINARY when creating a new BufferedImage to process the Image to grayscale and black/white, respectively.
Following these steps brought Tesseract's accuracy from a 30% to around an 85% when dealing with green text on black background, and a really-close-to-100% accuracy when dealing with normal black text on white background. (sometimes letters within a word are mistaken by numbers i.e. hel10)
I hope this helps!
Related
I am using tesseract 3.05 for reasons beyond my control. I am using source files to train the engine to detect this unique font. As I have a vast amount of samples, I am simply using the samples themselves as the training images rather than segment them into a font training image as this should give it more variation and training with the specific spacing issues this font has.
My question when generating the box files, as some letters are touching at corners (i.e . no clear break between glyphs), it will detect them as one glyph instead of two separate glyphs. An example it sometimes struggles with NA as the front serif of the A has bled into serif of the N. The image pre-processing I have applied has improved it by leaps and bounds but there are still some that I cannot correct on the image enough.
My question is this: can I simply denote the glyph as being NA in the box file?
If I cannot what would be the simplest solution? Introducing another glyph box seems like it wouldn't be a good idea but the only other solution I can see is to manually edit the image to make the separation of glyphs more obvious. This is itself anthi-thetical however as this is the kind of problem the font will have in the future that I am trying to OCR.
Thank you in advance but the documentation isn't specific on if I can correct a box glyph to being two characters instead of just one (or I just haven't found a relevant section where they explain this).
After scouring the documentation, I managed to find a lone paragraph that wasn't appearing in my website scraping:
"If you didn't successfully space out the characters on the training image, some may have been joined into a single box. In this case, you can either remake the images with better spacing and start again, or if the pair is common, put both characters at the start of the line, leaving the bounding box to represent them both. (As of 3.00, there is a limit of 24 bytes for the description of a "character". This will allow you between 6 and 24 unicodes to describe the character, depending on where your codes sit in the unicode set. If anyone hits this limit, please file an issue describing your situation.)"
Thus you can do what I ask: represent a glyph with two or more characters in a box file for Tesseract.
In the tesseract documentation a method of training with sample text and a font is explained.
I used jTessBoxEditor but works pretty much like the tesseract training tools.
I got somewhat acceptable results with this, but I guess the optimal solution would be training tesseract with the actual kind of images it will have to recognize anyway.
As I only need to recognize digits, I can cut by hand each of them, maybe many versions of each digit, and train tesseract with those images, even setting the boxes by hand.
Is there a way to do this?
If you are trying to train tesseract4, you can use ocrd-train
you basically prepare images corresponding to each line of text with their ground truth and it will do all the remaining work for you.
I have a problem with the training/text recognition process with Tesseract. Here is my trainingdata: http://s11.postimg.org/867aq10ur/dot_dotmatrixfont_exp0.png While training Tesseract ignores the dashes (I've marked them with red boxes, just to make it clear which ones I mean) and if I'm using the trained data for text recognition it also ignores them. Today I've played around with the Tesseract parameters (SetVariable(name, value)) but unfortunately I had no success.
What can I do to teach Tesseract those dashes? Thank you in advance!
Tesserect training is pretty tricky.
Your best chance might be to handle the dashes as a single char.
If your box editor or whatever tools you are using does not see the dashes as all, try running some image processing first, especially threshold or invert. try taking a look at OpenCV. They have some excellent tool for this kind of image processing.
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
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.)
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.
I'm writing a generator for training images for Tesseract OCR.
When generating a training image for a new font for Tesseract OCR, what are the best values for:
The DPI
The font size in points
Should the font be anti-aliased or not
Should the bounding boxes fit snugly: , or not:
The 2th question is somehow answered here: http://code.google.com/p/tesseract-ocr/wiki/TrainingTesseract3#Generate_Training_Images
There is no need to train with multiple sizes. 10 point will do. (An exception to this is very small text. If you want to recognize text with an x-height smaller than about 15 pixels, you should either train it specifically or scale your images before trying to recognize them.)
Questions 1 and 3: by experience, I've successfully used 300 dpi images/non anti-aliased fonts. More specifically, I have used the following convert parameters on a training pdf, which generated a satisfactory image:
convert -density 300 -depth 8 [input].pdf -background white -flatten +matte -compress none -monochrome [output].tif
But then I tried to add a dotted font to Tesseract and it only detected characters properly when I used a 150 dpi image. So, I don't think there's a general solution, it depends on the kind of fonts you're trying to add.
I found the answer to the 4th question - "Should the bounding boxes fit snugly".
It seems that fitting the rectangles as much as possible gives much better results.
For the other 12 pts and 300 dpi will be good enough, as #Yaroslav suggested. I think anti-aliasing is better turned off.
Good tool for tesseract training http://vietocr.sourceforge.net/training.html
It is good tool because having number of advantages
bounding box on letter can be editable by GUI based interface
automatically create all require file
automatically combined all files like freq-dawg, word-dawg, user-words (can be empty file), Inttemp, Normproto, Pffmtable, Unicharset, DangAmbigs (can be empty file), shapetable into single eng.traineddata file.
New training data can be used with existing tesseract file end.traineddata