I believe this is mostly gobbledygook. As Jim pointed out, the example is contrived. JPG artefacts are not, at least in my experience, the worst type of problems OCR systems have to contend with. I plunked the image -- both before and after -- into Photoshop to see what's what. Simply extracting the blue channel from the sample yielded results that were much improved. After that, a high-pass filter yielded pretty well perfect results. I do not believe that automated optimization of colour images for OCR is particularly interesting. There is likely the odd colour image out there, but I think they would be things that you would handle as a one-off as Jim suggested. I have had occasion to tinker with colour image manipulation in an effort to improve OCR. The thing with the use-case I had is that I was able to construct a customised series of filter steps to get what I needed, and then apply those to all the pages at once. I tinkered a lot with this one: http://www.archive.org/stream/waunangeeormassa00rich#page/49/mode/1up before attempting to clean up the rest of the pages. (BB, you should look at the IA OCR results on *that* page ;-)) What I found was that the foxing shows up most strongly in the blue channel, and if you select the red channel, you can get rid of it almost entirely. Next make a histogram of the pixel values. You expect a somewhat bi-modal distribution. A percentile cut is what I wound up with, to pick the cutoff between black and white. I think that a filter that performed principle component analysis on colourspace, used a statistical analysis of the results to find the colourspace axis that gives a pixel distribution resembling text, then a little high-pass filtering would do the job on what accusoft is going for. I also think the machine vision folks are way out in front. On 17-May-2010 19:44, Jim Adcock wrote:
http://www.accusoft.com/Improve_OCR_Accuracy_on_Color_Documents.pdf
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