Can I train tesseract using image/text pairs where the images and texts are just single words each? Most training examples I've seen on Github use lines, each line of text being an image with the correct text for that line. However I have a system which is already going to be producing word image/text pairs and I'd like to feed that back into training. Any reason why not? I know that there are page segmentation modes and that word segmentation and line segmentation are not the same thing. But I understand that psm only applies to inference and not training?
Update: I've posted this to the Tesseract github issues and the google group with no response there either. I'm not sure whether the question is badly formulated, or if it's just the case that noone knows the answer? I'm hoping that a bounty might encourage some input.
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I tried to improved the results of OpenSource OCR software. I'm using tessaract, because I find it still produces better results than gocr, but with bad quality input it has huge problems. So I tried to prepocess the image with various tools I found in the internet:
unpaper
Fred's ImageMagick Scripts: TEXTCLEANER
manuall using GIMP
But I was not able to get good results with this bad test document: (really just for test, I don't need to content of this file)
http://9gag.com/gag/aBrG8w2/employee-handbook
This online service works surprisingly good with this test document:
http://www.onlineocr.net/
I'm wonderung if it is possible using smart preprocessing to get similar results with tesseract. Are the OpenSource OCR engines really so bad compared to commercial ones? Even google uses tesseract to scan documents, so I was expecting more...
Tesseract's precision in recognition is a little bit lower than the precision of the best commercial one (Abbyy FineReader), but it's more flexible because of its nature.
This flexibility entail sometimes some preprocessing, because it's not possible for Tesseract to manage each situation.
Actually is used by google because is Google its main sponsor!
The first thing you could do is to try to expand the text in order to have at least 20 pixel wide characters or more. Since Tesseract works using as features the main segments of the characters' borders, it needs to have a bigger characters' size comparing with other algorithms.
Another thing that you could try, always referring to the test document you mentioned, is to binarize your image with an adaptive thresholding method (here you can find some infos about that https://dsp.stackexchange.com/a/2504), because some changes in the illumination are present. Tesseract binarizes the image internally, but this could be the case when it fails to do that (it's similar to the example here Improving the quality of the output with Tesseract, where you can also find some other useful informations)
I am working on a project that needs accurate OCR results for images with rich background. So I am comparing results of two OCRs (one of them is Tesseract) to make my choice. The point is that results are strongly affected by the pre-processing step and especially image binarization. I extracted the binarized image of the other OCR and passed it to Tesseract which enhanced the results of Tesseract by 30-40%.
I have two questions and your answers would be of much help to me:
What binarization algorithm does tesseract use, and is it configurable?
Is there a way to extract the binarized image of Tesseract OCR so I can test the other OCR with it?
Thanks in advance :)
I think I have found the answers to my questions:
1- The binarization algorithm used is Otsu thresholding. You can see it here in line 179.
2- To get the binarized image, a method in tesseract api can be called:
PIX* thresholded = api->GetThresholdedImage(); //thresholded must be freed
Otsu thresholding is a global filter. You can use some local filter to get better results.
You can look for Sauvalo's binarization see hereor Nick's here . Those both algorithm are Niblack's improvement.
I used it to binarize my image for an OCR and I get better result
Good luck
I'm working on getting the Lincoln font to work in Tesseract, and I'm getting abysmal results, even after going through the wildly complicated training process.
This is what the font looks like, so yeah, it's a bit tricky:
I've carefully made a training image, and then used that to make a box file. The training image is here (25MB!). The image is 300 DPI, and has representative characters nicely spaced out vertically and horizontally.
I made a box file for the training image, and it worked properly. I've verified that it's correct using a box file editor.
I took this box file/tif file, and used it to create training data. I did likewise with the 30 or so other sample images/fonts provided by Tesseract.
I created the unicharset file.
I created a font_properties file. There's no guidance on the site about when fraktur should be used. So I've tried it both this way (fraktur on for Lincoln):
eng.lincoln.box 0 0 0 0 1
And this way (fraktur off):
eng.lincoln.box 0 0 0 0 0
And finally, I've tried this with and without dictionary files. When I used dictionary files, they were the wordmap from my search engine, Sphinx, and they have about 15K common words and about 20K uncommon ones.
In all cases, when I try to OCR the first couple lines of this file (3MB), the quality is abysmal. Rather than getting:
United States Court of Appeals
for the Federal Circuit
I get:
OniteiJ %tates C0urt of QppeaIs
for the jfeI1eraICircuit
Why?
I think you'll need a lot more samples (letters) and better training images (clean background, grayscale, 300 DPI, etc.). And try to train with only one font (for instance, Lincoln) first. You can use jTessBoxEditor tool to generate your training images and edit the box files.
Once you master the training process, you can add other fonts to your training. You can test the success of the resultant language data by using it in performing OCR on the training image itself -- the recognition rates should be high.
The font names in font_properties should be like:
lincoln 0 0 0 0 1
I am not a Tesseract expert but I have evaluated nearly every OCR engine available and my comments are based on my experience over the years of analysing OCR errors.
Just wondering why your image has speckles in the background and not a pure white background. I don't know how Tesseract or the training tool works but the background could be causing some problems.
Just reading the sample page is difficult and requires a large amount of concentration. Characters such as F and I are very similar as are U and N. Tesseract like many OCR engines would be using many different techniques to recognise a character and there is not a whole lot difference between many of these characters in terms of the strokes and curves used in the font.
These characters, especially the uppercase characters would confuse many different matching algorithms just because they are so different to standard Latin / Roman type characters. This shows through in your results ie. All capital letters have an OCR error.
I'm trying to use Tesseract-OCR to detect the text of images with pure text in it but these text has a handwritten font called Journal.
Example:
The result is not the best:
Maxima! size` W (35)
Is there any possibility to improve the result or rather to get the exact result?
I am surprised Tesseract is doing so well. With a little bit of training you should be able to train the lower case 'l' to be recognised correctly.
The main problem you have is the top of the large T character. The horizontal line extends across 2 (possibly 3) other character cells and this would cause a problem for any OCR engine when it tries to segment the characters for recognition. Training may be able to help in this case.
The next problem is the . and : which are very light/thin and are possibly being removed with image pre-processing before the OCR even starts.
Overall the only chance to improve the results with Tesseract would be to investigate training. Here are some links which may help.
Alternative to Tesseract OCR Training?
Tesseract OCR Library learning font
Tesseract confuses two numbers
Like Andrew Cash mentioned, it'll be very hard to perform OCR for that T letter because of its intersection with a number of next characters.
For results improvement you may want to try a more accurate SDK. Have a look at ABBYY Cloud OCR SDK, it's a cloud-based OCR SDK recently launched by ABBYY. It's in beta, so for now it's totally free to use. I work # ABBYY and can provide you additional info on our products if necessary. I've sent the image you've attached to our SDK and got this response:
Maximal size: lall (35)
I've been reading (and trying) OCR programs suggested in previous answers but I'm still without a clear answer to my problem.
I need to recognize handwritten English text. The text would be multiple lines but each line is only one or two words length. The text is from a different person at time. I could ask that person to provide a training file (e.g. with the alphabet and 0-9 numbers) but I cannot really ask for a much more complicated training than this.
I need to integrate the recognition as part of another (Java) application but the solution doesn't need to be Java. I can just execute it from Java and get the results from a text file.
Any recommendations?
I've already tested Tesseract (bad results without training and training looks quite complex). Java OCR looked like the perfect solution (simple training, open source and Java) but it doesn't work well even with their own examples (anybody has had a better experiencie?). GOCR does not seem very active.
Of course I prefer free solutions but this is not a MUST (though the problem I see with a commercial option is that I must be able to integrate it in my own app which will be offered as SaaS)
From my experience ABBYY is one of the best for handwriting recognition, even without training. (It's possibly one of the most expensive too, though...) They have an SDK for Java.
http://www.abbyy.com
With a free trial, it's definately worth a look!
I am on the lookout for a handwritten text recognition software. So far the only one giving better results than even abby 11 has been SimpleOCR using the same text for both, which is a freeware for ocr but a 14 day trial for HCR!
I know I am answering after nearly 6 years. But if anyone's still looking, try using tensorflow. Their website has a simple example for handwritten digit recognition(MNIST). You can use this example and implement it for handwritten alphabet recognition (you need training data for this, I used NIST special Database 19 to get this data).