4 ms·

Last I compared them, (1-2 years ago), Google OCR was much much better and supported more languages than tesseract. There was also an OCR in openCV, which was s

by Icko 4y ago

Last I compared them, (1-2 years ago), Google OCR was much much better and supported more languages than tesseract. There was also an OCR in openCV, which was slightly better than tesseract, but not good enough to be useful.

rafram 4y ago
I’m not aware of any separate OCR in OpenCV. Some builds include an interface to Tesseract, which might be what you’re thinking of. Tesseract certainly benefits from preprocessing (conversion to grayscale, posterization) with OpenCV.
- Icko 4y ago
  There was "EAST OCR" detector, which is basically someone put a deep learning model in openCV somehow. https://www.folio3.ai/blog/text-detection-by-using-opencv-and-east/ https://www.folio3.ai/blog/text-detection-by-using-opencv-an...
lasagna_coder 4y ago
What are these projects are you referring to? AFAIK Tesseract is sponsored by Google, from what I understand it is state of the art, ie it is Google OCR. Searching for OCR with OpenCV only reveals using OpenCV with Tesseract, not rolling its own OCR, OpenCV being used to preprocess images to optimise them for Tesseract. Maybe I'm missing something, so I'm interested if you can point me in the right direction.
- spi 4y ago
  Google OCR is definitely not the same as Tesseract, although it's true that Tesseract is maintained by Google. Google OCR has definitely much higher accuracy and is significantly faster (basically always taking 1s for inference, while Tesseract can easily take 10s or more for dense pages). Source: I work in developing a competing OCR service and we keep an eye on competition (e.g. aside from Google, solutions by Azure, Amazon, Abbyy, Nuance, Cloudmersive, etc., as well as our internal product of course, which is not available externally), and they are (almost) all significantly better on Tesseract. The only domain where Tesseract is competitive is for perfect "black text on white paper", it gives pretty poor performance when dealing with colored, distorted text, or even strong page structure effects (tables, etc.). When I say "pretty poor" I mean: "with respect to the state-of-the-art", of course it's still enormously better than what was the state-of-the-art before deep learning came into the picture, roughly a decade ago. And for things like "search contents of a book" it's basically perfect already.
  - recuter 4y ago
    > Source: I work in developing a competing OCR service and we keep an eye on competition (e.g. aside from Google, solutions by Azure, Amazon, Abbyy, Nuance, Cloudmersive, etc., as well as our internal product of course, which is not available externally), and they are (almost) all significantly better on Tesseract. Great. How do you quantify it and keep track? Is there an industry standard benchmark? Would you consider sharing a backblaze type analysis (they track consumer HD performance and blogging about it got them a lot of attention and customers)?
    - deleted 4y ago
      [deleted]
    - spi 4y ago
      Sorry for the late answer. Short answer is: we can't and we don't. Most EULAs explicitly prevent users to benchmark results, and we don't want to incur into any such risk. Plus, since we develop a competing product, any "deep look" into the competition might be seen as reverse engineering it, and our company is very careful to avoid such problems. Our company has dedicated teams to evaluate competition products, so we once asked them (a couple of years ago), and could only look at aggregated, anonymized results. But the patterns were very clear. Anecdotical experience (mostly coming from customers of ours who, themselves, compare our internal engine with alternatives) seemed to point to the fact that most of the competition have rather stable service, so quality likely didn't evolve much in the last two years, but we can't be sure of course. We constantly track our own accuracy on internally developed benchmarks, because frankly the ones available online (also for research purposes) are very bad. But as said, we can only continuously test our own engine and open source ones (like Tesseract), for legal reasons.
      - recuter 4y ago
        Thank you kindly. :)
  - cinntaile 4y ago
    I don't get how it's a competing product if it's not available externally? What field is it if you can answer that?
holbue 4y ago
I agree, there are way better cloud based and proprietary OCR solutions out there. But Tesseract still seems to deliver the best results among the FOSS tools, doesn't it? Back in the days, Cuneiform got close to Tesseract's performance, but AFAIK it wasn't developed further... Does anyone else know other promising open-source OCR engines?

by Icko 4y ago

rafram 4y ago

I’m not aware of any separate OCR in OpenCV. Some builds include an interface to Tesseract, which might be what you’re thinking of. Tesseract certainly benefits from preprocessing (conversion to grayscale, posterization) with OpenCV.

Icko 4y ago
There was "EAST OCR" detector, which is basically someone put a deep learning model in openCV somehow. https://www.folio3.ai/blog/text-detection-by-using-opencv-and-east/ https://www.folio3.ai/blog/text-detection-by-using-opencv-an...

lasagna_coder 4y ago

What are these projects are you referring to? AFAIK Tesseract is sponsored by Google, from what I understand it is state of the art, ie it is Google OCR. Searching for OCR with OpenCV only reveals using OpenCV with Tesseract, not rolling its own OCR, OpenCV being used to preprocess images to optimise them for Tesseract. Maybe I'm missing something, so I'm interested if you can point me in the right direction.

spi 4y ago
Google OCR is definitely not the same as Tesseract, although it's true that Tesseract is maintained by Google. Google OCR has definitely much higher accuracy and is significantly faster (basically always taking 1s for inference, while Tesseract can easily take 10s or more for dense pages). Source: I work in developing a competing OCR service and we keep an eye on competition (e.g. aside from Google, solutions by Azure, Amazon, Abbyy, Nuance, Cloudmersive, etc., as well as our internal product of course, which is not available externally), and they are (almost) all significantly better on Tesseract. The only domain where Tesseract is competitive is for perfect "black text on white paper", it gives pretty poor performance when dealing with colored, distorted text, or even strong page structure effects (tables, etc.). When I say "pretty poor" I mean: "with respect to the state-of-the-art", of course it's still enormously better than what was the state-of-the-art before deep learning came into the picture, roughly a decade ago. And for things like "search contents of a book" it's basically perfect already.
- recuter 4y ago
  > Source: I work in developing a competing OCR service and we keep an eye on competition (e.g. aside from Google, solutions by Azure, Amazon, Abbyy, Nuance, Cloudmersive, etc., as well as our internal product of course, which is not available externally), and they are (almost) all significantly better on Tesseract. Great. How do you quantify it and keep track? Is there an industry standard benchmark? Would you consider sharing a backblaze type analysis (they track consumer HD performance and blogging about it got them a lot of attention and customers)?
  - deleted 4y ago
    [deleted]
  - spi 4y ago
    Sorry for the late answer. Short answer is: we can't and we don't. Most EULAs explicitly prevent users to benchmark results, and we don't want to incur into any such risk. Plus, since we develop a competing product, any "deep look" into the competition might be seen as reverse engineering it, and our company is very careful to avoid such problems. Our company has dedicated teams to evaluate competition products, so we once asked them (a couple of years ago), and could only look at aggregated, anonymized results. But the patterns were very clear. Anecdotical experience (mostly coming from customers of ours who, themselves, compare our internal engine with alternatives) seemed to point to the fact that most of the competition have rather stable service, so quality likely didn't evolve much in the last two years, but we can't be sure of course. We constantly track our own accuracy on internally developed benchmarks, because frankly the ones available online (also for research purposes) are very bad. But as said, we can only continuously test our own engine and open source ones (like Tesseract), for legal reasons.
    - recuter 4y ago
      Thank you kindly. :)
- cinntaile 4y ago
  I don't get how it's a competing product if it's not available externally? What field is it if you can answer that?

holbue 4y ago

I agree, there are way better cloud based and proprietary OCR solutions out there. But Tesseract still seems to deliver the best results among the FOSS tools, doesn't it? Back in the days, Cuneiform got close to Tesseract's performance, but AFAIK it wasn't developed further... Does anyone else know other promising open-source OCR engines?