Google Docs' OCR Quality Tested
orenh writes "Google has released a Google Docs application for Android, which includes the ability to create documents by OCR-ing photos. I tested the application's OCR quality and found that it's mediocre under the best conditions and poor under real-world conditions. However, I believe that this poor performance is caused in part by an intentional decision by Google."
There are a number of scanner apps in the market that do a much better job in the first step of this process, which is taking the picture. They then concentrate their efforts on producing a clean usable PDF of the document. I tested one of these and found that the PDF rendered by it was much better than the PDF produced by Google.
Everything is crisp and readable.
If the first fails, its no wonder the second OCR step fails.
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The end of the article is pretty telling. Basically any professional OCR software from the mid 1990's and normal consumer grade commercial software from today is lightyears ahead of open source solutions. Which is kind of sad, but the problem is that there really isn't a huge market for OCR in the way that there is for web browsers and other more successful projects, coupled with the inherent difficulty in doing good OCR.
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He uploaded the 120 dpi image instead of the 300 dpi image and is surprised the OCR sucks. Really? Lossy isn't the concern when you're OCR'ing bloack text on a white background. Seriously. Think about what the image is actually going to be used for, then make your decision.
And, seriously, how effective of OCR'ing are you really imagining you're going to get off of a camera phone pic, anyway?
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What article? The link seems to be pointing to a 403 Error page. At least to me.
I guess it'll be a little while before we'll see an app I'd wondered about. I thought it would be useful to be able to take snapshots of things like news reports (streamed on the web, El Gato Eye-TV domestic or satellite t.v., YouTube etc.) and do OCR on them, AND get an English translation of it. With the events so far this year, support for Japanese and Arabic languages would have been a good start.
If the increasing absurdity of the CAPTCHAs I tend to see is anything to go by, there are programs out there that'll read normal printed text from even the crappiest photo without missing a beat. The question is, are the spammers using standard commercial solutions, or have they got some useful tech of their own that we might be able to get our hands on (seize it as part of a settlement and make it public domain, for instance).
Google took the Tesseract OCR engine, one of the first engines, and wrapped document analysis and some high level improvements on it. In the current OCR market landscape there are only 4 commercial engines, and two that make up 98% of the market. Compared to those two OCROpus is not even close because of the legacy engine. So the real reason is it's old technology, very old. Unless Google licenses ABBYY or Nuance they will not get any better. The reality is OCR takes 50 man-years to develop to compete with these top two engines, and it's just not practical for even Google to go out and start from scratch.
I don't think that spammers have any amazing tech, they just have different requirements. They can still send spam with a 1% success rate whereas with OCR you'd want a 99% success rate.
I once worked on an OCR project. The client specified a 99% success rate and we strained to restrain our grins. 99% is about one error every one or two lines of text. We got 99.6% in our first implementation before we even began to work on accuracy. Admittedly we had excellent image quality. This was a custom solution that had its own optics.
Does that mean it couldn't be a viable candidate for some Summer of Code work then?
More like a bunch of masters/phd thesis to get started.
OCR is an area of AI research under the topic of Computer Vision. It is yet another area that seems simple in concept but turns out to be incredibly difficult in practice.
seems to me that OCR would be an area that would be easy to build a framework for genetic algorithms, using a huge collection of solved OCR pages to evaluate. with each generation being tested on a random subset of pages so they do not learn to cheat instead of learn to solve.
only problem is sometimes GA make a solution that makes no sense and should not work but somehow does http://www.damninteresting.com/on-the-origin-of-circuits
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I think the quality is tolerable. I photographed a document lying on my desk, without doing anything special to make it smooth or adjust lighting. This is a good simulation of a real-world situation where you can photograph a piece of text. There were errors in the transcription but it was readable, and with a very little editing would have been perfect. What surprised me was that apparently the whole image was uploaded from my phone to Google Docs, and then downloaded again, which is a little bit inefficient; I think that the OCR process runs server side.
I see this as very useful. This afternoon I'm going in to the local planning office to look at some planning applications; I won't be able to take them away, and I doubt I'll be allowed to use a photocopier, but I will have my phone. That's a real world application. I can think of hundreds more.
I'm old enough to remember when discussions on Slashdot were well informed.