Tesseract makes different predictions on seemingly equal images. How to make it more robust?

[MysteriousGuy]

I am using Tesseract to extract text from images attached. For some reason, even though the images are nearly identical, tesseract makes a mistake in one of them: for 'bad.png' the output is ELHADIJ, whereas for 'good.png' it is ELHADJ

Here is what I have and done:

• tesseract version: 4.0.0-beta.1
• leptonica version: 1.75.3
• I use English .traineddata file from here: https://github.com/tesseract-ocr/tessdata_best/blob/master/eng.traineddata
• I tried these page segmentation modes: 3, 7, 8, 13 - the mistake is always there.

So the commands I ran were

tesseract good.png output1 -l eng --psm 8

tesseract bad.png output2 -l eng --psm 8

and similarly for other PSMs

My question is: how do I make tesseract more robust? Why does it make a mistake in one case but not in the other?

[Zdenko Podobny]

Try to use the latest version of tesseract.

Zdenko

ut 14. 7. 2020 o 16:04 MysteriousGuy <gyt...@gmail.com> napísal(a):

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[MysteriousGuy]

Hi. Latest stable version (4.1.1) produces the same error

2020 m. liepa 14 d., antradienis 17:13:40 UTC+3, zdenop rašė:

Try to use the latest version of tesseract.

Zdenko

ut 14. 7. 2020 o 16:04 MysteriousGuy <gyt...@gmail.com> napísal(a):

I am using Tesseract to extract text from images attached. For some reason, even though the images are nearly identical, tesseract makes a mistake in one of them: for 'bad.png' the output is ELHADIJ, whereas for 'good.png' it is ELHADJ

Here is what I have and done:

• tesseract version: 4.0.0-beta.1
• leptonica version: 1.75.3
• I use English .traineddata file from here: https://github.com/tesseract-ocr/tessdata_best/blob/master/eng.traineddata
• I tried these page segmentation modes: 3, 7, 8, 13 - the mistake is always there.

So the commands I ran were

tesseract good.png output1 -l eng --psm 8

tesseract bad.png output2 -l eng --psm 8

and similarly for other PSMs

My question is: how do I make tesseract more robust? Why does it make a mistake in one case but not in the other?

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[Tuan Ardouin]

You need to apply some pre-processing to your image.

[MysteriousGuy]

This seems like an ad-hoc approach. I am already converting images to grayscale. If I apply blurring, binarisation, etc. then I will solve this case but I will prompt another case to fail as a result. There is something with tesseract that fails to generalize on clearly near-identical images, and I am interested in what is it.

[Lorenzo Bolzani]

 I think the reason is that your input is bad so the model is confused and a few pixels are enough to see an extra letter.

Your input is "bad" because it is different from the one used to train the neural network. The difference between the two images is small but the difference from the training data for both is big.

If you improve your image with zero borders, less noise and a much stronger contrast, maybe even straighten the text this kind of problem should become much less common.

If you want to understand a little more why this is possible read something about how an LSTM ocr works. This is likely something in the step that tries to decide the letters from the neural network output (beam search, CTC). Not a bug just how it works.

I do not think there is much you can do, parameters, etc., other than improve your image or tesseract. Sometimes it happens even with fine tuned models.

Lorenzo

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