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I had to research the commercial OCR market recently for a client project. It's abysmal. Most of the solutions are horrendously overpriced, windows only enterpr

by pault 9y ago

I had to research the commercial OCR market recently for a client project. It's abysmal. Most of the solutions are horrendously overpriced, windows only enterprise packages, and the ones that I was able to try out bordered on unusable (bad results and terrible UI). Since OCR is basically "hello world" for tensorflow, I don't understand why these incumbents haven't been wiped off the map.

bflesch 9y ago
Because for example Abby replaces the image with an PDF with invisible text overlays. I think it is a bit more complicated once you account for the formatting of documents.
- milesokeefe 9y ago
  Abbyy has had that ability for a longer period of time so it may be more accurate, but this is something tesseract supports[1]. In fact I'd say most OCR systems support it with varying degrees of accuracy. [1]:https://github.com/tesseract-ocr/tesseract/wiki/FAQ#how-do-i-produce-searchable-pdf-output https://github.com/tesseract-ocr/tesseract/wiki/FAQ#how-do-i...
  - deleted 9y ago
    [deleted]
- pault 9y ago
  My issue was that I needed one that exposes an API for doing OCR on pictures taken from mobile devices. It was really difficult to find non-desktop packages.
  - staticautomatic 9y ago
    Out of curiosity, what did you need beyond something like OpenCV/Metal + Tesseract?
    - pault 9y ago
      The client needed an OCR solution for supplier invoices with a variety of layouts and a combination of printed and hand-written characters, and didn't have the budget for a bespoke solution. To be fair, it's a very hard problem, I was just surprised that given all the much hyped recent advancements in deep learning for computer vision, most of the solutions in the market seem to be running on decades old technology.
      - ocrcustomserver 9y ago
        Well it is more complex than it appears. Extracting data from documents requires a solution which uses OCR but is a different product (e.g. ABBYY FlexiCapture). This is most commonly referred to as zonal OCR and comes with the added functionality of handling multiple templates, defining zones/fields, specifying special rules for fields, verification process for manual inspection (e.g. triggered when the image receives a low confidence recognition score) etc. This is different and more complex than a product that does full page OCR (e.g. ABBYY Finereader). Handwritten OCR is a whole different story. The products that do zonal OCR will fail to recognize handwritten text, unless it's in boxes (PDF forms). I'm working on a prototype that can handle handwritten text outside of boxes too.
- RandomBookmarks 9y ago
  The ability to create searchable PDFs is very useful and convenient. But creating searchable PDFs does not require a deep understanding of the document format (like column detection etc). You just place the words at the right coordinates of their bounding boxes. You can test this for example here: https://ocr.space https://ocr.space - select the option to create a searchable PDF. It works even for the most complex documents. Now, creating a Word document from a scan is a different beast because it requires layout analysis. This is where Abbyy with its long experience still has a good lead.
phren0logy 9y ago
I recently looked at a number of packages, and the best option in my opinion is ABBYY Finereader Pro for Mac. The mac version can easily be automated with Hazel or Automator, which adds features that cost +$400 more for the PC. The results are worlds better than Acrobat.
- philipkglass 9y ago
  When I did my big project I was using the cheap standard edition of Finereader on (virtualized) Windows, and I built an automated pipeline for it with a combination of AutoHotkey, pdftk, Multivalent pdf tools, and Python. I too tried the Acrobat OCR first and found it to be vastly inferior. One surprise was that (at the time at least) the OCR-by-Acrobat files were not only much less accurate in the text layer, but the file size also bloated up surprisingly.
acdha 9y ago
> Since OCR is basically "hello world" for tensorflow, I don't understand why these incumbents haven't been wiped off the map. Do you have an example of anyone working on that? I think those packages have a lot of inertia because you can buy a product with an API, documentation, etc. Something which says “Learn TensorFlow & enough AI to be dangerous” is going to be hard to get in the door at many large organizations so this might be an area where a solid open-source project could have a significant impact.
- slap_shot 9y ago
  The parent post is not implying that companies adopt Tensorflow themselves and solve this problem. The post is implying that a competitor come in to the market using the newer, better technology, and replace them with a better product (with an API, documentation, etc). This issue here is that industries become stagnant when it appears difficult/costly to introduce a new product in that market. You usually need significant a technical and/or social change to occur before incumbents can be displaced. In this case, it was a technical change. The other part of the issue is that investors want "0 to 1" ideas to invest in, and founders often chase those ideas. In reality, we need more "1.0 to 1.5" products, especially in crowded markets.
  - acdha 9y ago
    I think your last point is correct: there's a huge gap between the kind of “hello world” demo the original poster was talking about and a product-level tool which has reasonable training data and performance across a non-trivial range of documents. I think that getting there is a lot more work than OP anticipated.
    - pault 9y ago
      OP here; yes, you've summed it up perfectly.
sidlls 9y ago
OCR for really basic documents might be "hello world", but as usual real world practical applications have all sorts of interesting things that make this a harder problem than it seems.
staticautomatic 9y ago
Lots of reasons they haven't been wiped off the map. 1. Full page OCR is trivial these days. Anyone who's just doing full-page OCR has no business using one of these commercial offerings. The commercial stuff is really for extraction of structured info from unstructured and semi-structured documents. ABBYY and Nuance are the only ones with products that can handle it. There are alternatives for simple capture tasks (like DocParser), but not for complex ones. 2. ABBYY and Nuance have a lot of IP in the space on lock. 3. The market for complex data extraction (at least the kind of stuff I do) may not actually be big enough for smaller players to bother pursuing (most new players are going after tasks of intermediate difficulty, like invoice and receipt capture). 4. There's still a need for on-prem style solutions for people doing huge volumes (hundreds of thousands of pages a year), whereas most of the new market entrants are cloud only. 5. I haven't used Nuance's stuff much but ABBYY's products are actually incredibly robust under the hood-- stuff you wouldn't want to build yourself unless you absolutely had to. FlexiCapture will do 95%+ accuracy out of the box (on a character by character basis) with no human verification.
- derefr 9y ago
  > Anyone who's just doing full-page OCR has no business using one of these commercial offerings. So what are you supposed to use for full-page OCR?
  - ocrcustomserver 9y ago
    For an opensource solution that uses Tesseract, check out ocrmypdf: https://github.com/jbarlow83/OCRmyPDF https://github.com/jbarlow83/OCRmyPDF
  - rb2018 9y ago
    As far as hosted solutions go, the best are Google Cloud Vision, Azure OCR and OCr.space You can compare all three here: https://ocr.space/compare-ocr-software https://ocr.space/compare-ocr-software
- ocrcustomserver 9y ago
  I wouldn't say that full page OCR is trivial. Using an opensource solution (99% based on Tesseract) is going to get you ok-ish results if your input is relatively clean (no complex layout, scanned documents from a flatbed scanner, standard fonts) and you don't care about speed. If you care about recognition accuracy then Tesseract isn't going to cut it (at least not without some serious effort). Replying to points 1 and 3: For smaller players and/or complex tasks you can always implement your own custom parser. I'm doing work as a contractor in this space.
computerex 9y ago
OCR is not "hello world" for tensorflow. The entire article is devoted to actually developing the OCR pipeline. They ported the model over to tensorflow as a secondary thing.
criddell 9y ago
Do you know what Evernote uses? Although I don't really use it anymore, when I did, I was astonished at how will it found and indexed text.
- jhayward 9y ago
  If I recall, they don't do traditional OCR, rather they build a term probability database that lets them do matching on search terms. Both Evernote and ABBYY seem to have ties to a specific Russian research group.
- ocrcustomserver 9y ago
  They use multiple OCR engines. Some developed in-house and some proprietary ones. A blog post mentions I.R.I.S. as one of the proprietary ones. They don't offer OCR publicly, instead, they generate a list of possible candidates to match against search queries (fuzzy matching). From one of the blog posts: "Employing multiple reco engines is important not only because they specialize on different types of text and languages, but also as this allows to implement ‘voting’ mechanism — analyzing word alternatives created by diverse engines for the same word allows for better suppression of false recognitions and giving more confidence to consensus variants." More: http://blog.evernote.com/tech/2013/07/18/how-evernotes-image-recognition-works/ http://blog.evernote.com/tech/2013/07/18/how-evernotes-image... http://blog.evernote.com/tech/2011/11/01/even-grittier-details-on-evernotes-indexing-system/ http://blog.evernote.com/tech/2011/11/01/even-grittier-detai... http://blog.evernote.com/tech/2011/09/30/evernote-indexing-system/ http://blog.evernote.com/tech/2011/09/30/evernote-indexing-s...
wwarner 9y ago
Character recognition might be "hello world" but full page OCR definitely is not. When I read the article and think about all the obstacles dropbox overcame, it looks like a pretty huge achievement. I can tell you, it would take me a lot longer than 8 months do pull it off.
- Drdrdrq 9y ago
  I am certain they had a team on this, I just wonder what its size was. Quite an achievement!
amelius 9y ago
> Since OCR is basically "hello world" for tensorflow [...] In that case ... it would be nice if Tensorflow had OCR as one of its examples/tutorials.
romuloab42 9y ago
I know you are talking about Desktop OCR, but I had a very good experience with Google Vision API. On my previous gig we were trying to automate receipt scanning, and it gave very good results with no previous image manipulation whatsoever (meaning, no special camera alignment, lighting condition, rotation, skewing, etc). It was not perfect, but I was very impressed with the quality, speed, and price.
ocrcustomserver 9y ago
Some thoughts (adding to staticautomatic's post): 1. There is no dataset/competition like ImageNet for OCR. 2. Most people/conferences/universities are going after natural images and "computer vision" problems. OCR is its own animal and while it shares some concepts with computer vision it's not the same thing. 3. A lot of IP, knowledge and talent locked up in a handful of very old companies doing this for a long time. ABBYY is for OCR what Google + Facebook are for deep learning (maybe more). 4. OCR is kind of a niche, a lot of knowledge is not available to many people outside of a few insiders (ABBYY/Nuance, universities, research labs, OCR conferences). I'm sure Google uses it a lot internally (e.g. Google Street View numbers etc.). 5. The incumbents don't just do OCR. They do preprocessing (computer vision/image processing) + OCR + NLP. 6. Hard to find data. ABBYY Finereader supports 190 languages. Collecting this data is no easy task. I'm probably missing other reasons as well, but this is just off the top of my head. That being said, I'm sure that there's going to be a lot of progress in the OCR + deep learning space soon.
rb2018 9y ago
>Since OCR is basically "hello world" for tensorflow, This is a strong statement. Do you have any links to code or documentation that implements high-quality OCR with tensorflow?
- pault 9y ago
  Not high quality. But since MNIST is typically used as a baseline reference, I was surprised that more advanced character recognition wasn't a staple in the field. From the tensorflow introduction[0]: "When one learns how to program, there's a tradition that the first thing you do is print 'Hello World.' Just like programming has Hello World, machine learning has MNIST." I realize that this is a result of my lack of knowledge about the field, but the ML hype train makes it easy to overestimate the capabilities of deep learning as a layperson. [0] https://www.tensorflow.org/get_started/mnist/beginners https://www.tensorflow.org/get_started/mnist/beginners

by pault 9y ago

bflesch 9y ago

Because for example Abby replaces the image with an PDF with invisible text overlays. I think it is a bit more complicated once you account for the formatting of documents.

milesokeefe 9y ago
Abbyy has had that ability for a longer period of time so it may be more accurate, but this is something tesseract supports[1]. In fact I'd say most OCR systems support it with varying degrees of accuracy. [1]:https://github.com/tesseract-ocr/tesseract/wiki/FAQ#how-do-i-produce-searchable-pdf-output https://github.com/tesseract-ocr/tesseract/wiki/FAQ#how-do-i...
- deleted 9y ago
  [deleted]
pault 9y ago
My issue was that I needed one that exposes an API for doing OCR on pictures taken from mobile devices. It was really difficult to find non-desktop packages.
- staticautomatic 9y ago
  Out of curiosity, what did you need beyond something like OpenCV/Metal + Tesseract?
  - pault 9y ago
    The client needed an OCR solution for supplier invoices with a variety of layouts and a combination of printed and hand-written characters, and didn't have the budget for a bespoke solution. To be fair, it's a very hard problem, I was just surprised that given all the much hyped recent advancements in deep learning for computer vision, most of the solutions in the market seem to be running on decades old technology.
    - ocrcustomserver 9y ago
      Well it is more complex than it appears. Extracting data from documents requires a solution which uses OCR but is a different product (e.g. ABBYY FlexiCapture). This is most commonly referred to as zonal OCR and comes with the added functionality of handling multiple templates, defining zones/fields, specifying special rules for fields, verification process for manual inspection (e.g. triggered when the image receives a low confidence recognition score) etc. This is different and more complex than a product that does full page OCR (e.g. ABBYY Finereader). Handwritten OCR is a whole different story. The products that do zonal OCR will fail to recognize handwritten text, unless it's in boxes (PDF forms). I'm working on a prototype that can handle handwritten text outside of boxes too.
RandomBookmarks 9y ago
The ability to create searchable PDFs is very useful and convenient. But creating searchable PDFs does not require a deep understanding of the document format (like column detection etc). You just place the words at the right coordinates of their bounding boxes. You can test this for example here: https://ocr.space https://ocr.space - select the option to create a searchable PDF. It works even for the most complex documents. Now, creating a Word document from a scan is a different beast because it requires layout analysis. This is where Abbyy with its long experience still has a good lead.

phren0logy 9y ago

I recently looked at a number of packages, and the best option in my opinion is ABBYY Finereader Pro for Mac. The mac version can easily be automated with Hazel or Automator, which adds features that cost +$400 more for the PC. The results are worlds better than Acrobat.

philipkglass 9y ago
When I did my big project I was using the cheap standard edition of Finereader on (virtualized) Windows, and I built an automated pipeline for it with a combination of AutoHotkey, pdftk, Multivalent pdf tools, and Python. I too tried the Acrobat OCR first and found it to be vastly inferior. One surprise was that (at the time at least) the OCR-by-Acrobat files were not only much less accurate in the text layer, but the file size also bloated up surprisingly.

acdha 9y ago

> Since OCR is basically "hello world" for tensorflow, I don't understand why these incumbents haven't been wiped off the map. Do you have an example of anyone working on that? I think those packages have a lot of inertia because you can buy a product with an API, documentation, etc. Something which says “Learn TensorFlow & enough AI to be dangerous” is going to be hard to get in the door at many large organizations so this might be an area where a solid open-source project could have a significant impact.

slap_shot 9y ago
The parent post is not implying that companies adopt Tensorflow themselves and solve this problem. The post is implying that a competitor come in to the market using the newer, better technology, and replace them with a better product (with an API, documentation, etc). This issue here is that industries become stagnant when it appears difficult/costly to introduce a new product in that market. You usually need significant a technical and/or social change to occur before incumbents can be displaced. In this case, it was a technical change. The other part of the issue is that investors want "0 to 1" ideas to invest in, and founders often chase those ideas. In reality, we need more "1.0 to 1.5" products, especially in crowded markets.
- acdha 9y ago
  I think your last point is correct: there's a huge gap between the kind of “hello world” demo the original poster was talking about and a product-level tool which has reasonable training data and performance across a non-trivial range of documents. I think that getting there is a lot more work than OP anticipated.
  - pault 9y ago
    OP here; yes, you've summed it up perfectly.

sidlls 9y ago

OCR for really basic documents might be "hello world", but as usual real world practical applications have all sorts of interesting things that make this a harder problem than it seems.

staticautomatic 9y ago

Lots of reasons they haven't been wiped off the map. 1. Full page OCR is trivial these days. Anyone who's just doing full-page OCR has no business using one of these commercial offerings. The commercial stuff is really for extraction of structured info from unstructured and semi-structured documents. ABBYY and Nuance are the only ones with products that can handle it. There are alternatives for simple capture tasks (like DocParser), but not for complex ones. 2. ABBYY and Nuance have a lot of IP in the space on lock. 3. The market for complex data extraction (at least the kind of stuff I do) may not actually be big enough for smaller players to bother pursuing (most new players are going after tasks of intermediate difficulty, like invoice and receipt capture). 4. There's still a need for on-prem style solutions for people doing huge volumes (hundreds of thousands of pages a year), whereas most of the new market entrants are cloud only. 5. I haven't used Nuance's stuff much but ABBYY's products are actually incredibly robust under the hood-- stuff you wouldn't want to build yourself unless you absolutely had to. FlexiCapture will do 95%+ accuracy out of the box (on a character by character basis) with no human verification.

derefr 9y ago
> Anyone who's just doing full-page OCR has no business using one of these commercial offerings. So what are you supposed to use for full-page OCR?
- ocrcustomserver 9y ago
  For an opensource solution that uses Tesseract, check out ocrmypdf: https://github.com/jbarlow83/OCRmyPDF https://github.com/jbarlow83/OCRmyPDF
- rb2018 9y ago
  As far as hosted solutions go, the best are Google Cloud Vision, Azure OCR and OCr.space You can compare all three here: https://ocr.space/compare-ocr-software https://ocr.space/compare-ocr-software
ocrcustomserver 9y ago
I wouldn't say that full page OCR is trivial. Using an opensource solution (99% based on Tesseract) is going to get you ok-ish results if your input is relatively clean (no complex layout, scanned documents from a flatbed scanner, standard fonts) and you don't care about speed. If you care about recognition accuracy then Tesseract isn't going to cut it (at least not without some serious effort). Replying to points 1 and 3: For smaller players and/or complex tasks you can always implement your own custom parser. I'm doing work as a contractor in this space.

computerex 9y ago

OCR is not "hello world" for tensorflow. The entire article is devoted to actually developing the OCR pipeline. They ported the model over to tensorflow as a secondary thing.

criddell 9y ago

Do you know what Evernote uses? Although I don't really use it anymore, when I did, I was astonished at how will it found and indexed text.

jhayward 9y ago
If I recall, they don't do traditional OCR, rather they build a term probability database that lets them do matching on search terms. Both Evernote and ABBYY seem to have ties to a specific Russian research group.
ocrcustomserver 9y ago
They use multiple OCR engines. Some developed in-house and some proprietary ones. A blog post mentions I.R.I.S. as one of the proprietary ones. They don't offer OCR publicly, instead, they generate a list of possible candidates to match against search queries (fuzzy matching). From one of the blog posts: "Employing multiple reco engines is important not only because they specialize on different types of text and languages, but also as this allows to implement ‘voting’ mechanism — analyzing word alternatives created by diverse engines for the same word allows for better suppression of false recognitions and giving more confidence to consensus variants." More: http://blog.evernote.com/tech/2013/07/18/how-evernotes-image-recognition-works/ http://blog.evernote.com/tech/2013/07/18/how-evernotes-image... http://blog.evernote.com/tech/2011/11/01/even-grittier-details-on-evernotes-indexing-system/ http://blog.evernote.com/tech/2011/11/01/even-grittier-detai... http://blog.evernote.com/tech/2011/09/30/evernote-indexing-system/ http://blog.evernote.com/tech/2011/09/30/evernote-indexing-s...

wwarner 9y ago

Character recognition might be "hello world" but full page OCR definitely is not. When I read the article and think about all the obstacles dropbox overcame, it looks like a pretty huge achievement. I can tell you, it would take me a lot longer than 8 months do pull it off.

Drdrdrq 9y ago
I am certain they had a team on this, I just wonder what its size was. Quite an achievement!

amelius 9y ago

> Since OCR is basically "hello world" for tensorflow [...] In that case ... it would be nice if Tensorflow had OCR as one of its examples/tutorials.

romuloab42 9y ago

I know you are talking about Desktop OCR, but I had a very good experience with Google Vision API. On my previous gig we were trying to automate receipt scanning, and it gave very good results with no previous image manipulation whatsoever (meaning, no special camera alignment, lighting condition, rotation, skewing, etc). It was not perfect, but I was very impressed with the quality, speed, and price.

ocrcustomserver 9y ago

Some thoughts (adding to staticautomatic's post): 1. There is no dataset/competition like ImageNet for OCR. 2. Most people/conferences/universities are going after natural images and "computer vision" problems. OCR is its own animal and while it shares some concepts with computer vision it's not the same thing. 3. A lot of IP, knowledge and talent locked up in a handful of very old companies doing this for a long time. ABBYY is for OCR what Google + Facebook are for deep learning (maybe more). 4. OCR is kind of a niche, a lot of knowledge is not available to many people outside of a few insiders (ABBYY/Nuance, universities, research labs, OCR conferences). I'm sure Google uses it a lot internally (e.g. Google Street View numbers etc.). 5. The incumbents don't just do OCR. They do preprocessing (computer vision/image processing) + OCR + NLP. 6. Hard to find data. ABBYY Finereader supports 190 languages. Collecting this data is no easy task. I'm probably missing other reasons as well, but this is just off the top of my head. That being said, I'm sure that there's going to be a lot of progress in the OCR + deep learning space soon.

rb2018 9y ago

>Since OCR is basically "hello world" for tensorflow, This is a strong statement. Do you have any links to code or documentation that implements high-quality OCR with tensorflow?

pault 9y ago
Not high quality. But since MNIST is typically used as a baseline reference, I was surprised that more advanced character recognition wasn't a staple in the field. From the tensorflow introduction[0]: "When one learns how to program, there's a tradition that the first thing you do is print 'Hello World.' Just like programming has Hello World, machine learning has MNIST." I realize that this is a result of my lack of knowledge about the field, but the ML hype train makes it easy to overestimate the capabilities of deep learning as a layperson. [0] https://www.tensorflow.org/get_started/mnist/beginners https://www.tensorflow.org/get_started/mnist/beginners