In many cases, Tesseract OCR's internal image pre-processing is degrading the quality of the image. I want to control the image processing before feeding to Tesseract OCR and disable the OCR's internal pre-processing steps.
I am not sure what do you mean with "degrading the quality of the image", but tesseract use binarized image for OCR, so if you provide binarized image as input, you can skip tesseract internal function for this. AFAIK there is no other preprocessing in tesseract (user has to do preprocessing).
Related
does Tesseract have a pipeline structure? If so, how can I add/remove a pipeline? Can I output the preprocessed Tesseract image to use it for other purposes?
Why is Tesseract OCR engine using a global thresholding technique such as Otsu binarization? Aren't local thresholding techniques (e.g. Sauvola, Niblack, etc.) more effective in leaving out text from images?
Tesseract was used in Google book project and AFAIK they run tests for best binarization and Otsu was most universal. If Otsu is not best for your case you can use other binarization algorithm before sending image to tesseract.
Basically, depending on the input image we need to select which thresholding algorithm to use. Tesseract uses Otsu method for thresholding because generally the input to Tesseract for extracting the text is having image homogeneities. Otsu method is efficient as well as good enough for such images.
Global thresholding method is useful and good enough when the background does not show local variation relative to the foreground (target) intensity. While local thresholding is necessary when there is local variation occurring between the intensity difference of background and target.
So, while Tesseract does use Otsu method (global thresholding) for binarization, you could pre-process the image with local thresholding methods to get better output from Tesseract.
Through Homebrew, I have installed the Tesseract OCR engine on my Mac. All the directories (jpeg, leptonica, libpng, libtiff, openssl, tesseract) are now installed in /usr/local/Cellar
Before putting an image in the Cellar directory, when I try the following at the command line, obviously it fails:
$ tesseract image.png outcome
So, because there is no such image, I get the following error messages:
Error in fopenReadStream: file not found
Error in findFileFormat: image file not found
Error during processing.
Where are the programs/scripts that generate these messages? I can only find include files in the installed Tesseract directory... Where are the files that contain these error messages strings if the image was not found, etc...?
Also, where are the scripts/programs that perform image pre-processing (such as segmentation, binarization, noise removal, etc...) before Tesseract actually does the OCR on the image?
Context/Background
We are planning to improve (rather customize) Tesseract for our needs (for example recognizing products' serial numbers and vehicle number plates) but obviously first we need to know what kind of filtering and thresholding Tesseract does by default.
I understand Tesseract performs various image-processing operations internally (using the Leptonica library) before doing the actual OCR. For example, I understand Tesseract does Binarization and Segmentation and Noise Removal internally, as well as having a default segmentation method. Is this right? Which script(s) contain these methods, so that I can see in what order these internal image-processing operations are carried out before doing the actual OCR?
The github download has a lot of directories and code, so I would really appreciate someone pointing us in the right direction -- where we should look to see the standard parameters and image-processing operations that Tesseract does before doing the actual OCR. We can only find .h header files...
Thanks,
I'm still new to Tesseract OCR and after using it in my script noticed it had a relatively big error rate for the images I was trying to extract text from. I came across Tesseract training, which supposedly would be able to decrease error rate for a specific font you'd use. I came across a website (http://ocr7.com/) which is a tool powered by Anyline to do all the training for a font you specify. So I recieved a .traineddata file and I am not quite sure what to do with it. Could anybody explain what I have to do with this file for it to work? Or should I just learn how to do Tesseract training the manual way, which according to the Anyline website may take a day's work. Thanks in advance.
For anyone that is still going to read this, you can use this tool to get a traineddata file of whichever font you want. After that move the traineddata file in your tessdata folder. To use tesseract with the new font in Python or any other language (I think?) put lang = "Font"as second parameter in image_to_string function. It improves accuracy significantly but can still make mistakes ofcourse. Or you can just learn how to train tesseract for a new font manually with this guide: http://pretius.com/how-to-prepare-training-files-for-tesseract-ocr-and-improve-characters-recognition/.
Edit:
Tesseract 5 training tutorial: https://www.youtube.com/watch?v=KE4xEzFGSU8
I made a video tutorial explaining the process for the latest version of Tesseract (The LSTM model), hope it helps. https://www.youtube.com/watch?v=TpD76k2HYms
If you want to train tesseract with the new font, then generate .traineddata file with your desired font. For generating .traineddata, first you will need .tiff file and .box file. You can create these files using jTessBoxEditor. Tutorial for jBossTextEditor is here. While making .tiff file you can set the font in which you have train tesseract. Either you can jTessBoxEditor for generating .traineddata or serak-tesseract-trainer is also there. I have used both and I would say that for generating tiff and box files jTessBoxEditor is great and for training tesseract use serak.
I have lots of images which only have 1 single character, how can I use them to train my own tessdata library on batch ? Is there any tips?
2.
And besides,
I'm confused with the feature extraction part between library training and character recognization ? Could anyone explained the flow ?
Thanks very much!
If they are of same font, put them in a multi-page TIFF and conduct training on it. jTessBoxEditor can help you with the TIFF merging and box editing.