Hacker News Reader

Comment by prashnts | Hacker News Reader

Since I am working on a similar problem at the moment myself, It'd be great if you could share some insights on fixing the 3D deformation -- I imagine fitting a polygon followed with a warp transformation could be an "ideal" process?

In the contrast problem you mention there, I found (in a few samples that I tested with) that adaptive thresholding seem to be sufficiently good [0].

[0] I am using ``skimage.filters.threshold_adaptive`` for this.

View on HN

On the contrast topic: adaptive thresholding can be very helpful (I believe Bradley Local Thresholding was one I had particular success with) however most of these algorithms work in a grayscale domain which means they are dependent upon which color->grayscale transformation is used[1]. I spent a long time researching full color algorithms but never got to a truly successful end result with them. And even if you get a good image with huge contrast you still will end up with the actual light/dark transition looking like an edge.

On 3D deformation, you're officially in academic research land. Nearly all algorithms require you to have a solid guess as to what the aspect ratio of the target object is. Other algorithms use heuristics based upon what you expect to find on a page. One particularly fun algorithm used the baseline of text (I believe for that paper it was Arabic) and fit a high-order curve to it which was then reversed. Unfortunately I haven't seen a truly generic approach that doesn't require a implementation-specific input.

[1] Frankly my feeling is that RGB to grayscale is a mistake and holding back many of these algorithms

I have found colorspace transformation to be an important factor as well. My current problem would not require fixing 3D deformation, but I am finding it really interesting thing that I'd like to be working on in future.

Thanks for this additional information, much appreciated!

Yep, we turned RGB into LUV space before extracting edges, which helps a lot on contrast and keeps essential edge information that could've been lost if converted to grayscale.

Agree with that 3D deformation is a difficult open problem, and we haven't gotten into that yet. Currently we assumed the document is a flat rectangle, which maps to a quadrilateral in image space. A homography is then applied to rectify it, and it seems to work quite well if the paper is slightly curved or folded.

Excellent. It's a little funny how when you start problems like these you start becoming an expert in fields you never thought you'd have to play in like color spaces, color perception theory, etc.

Great work, and I look forward to seeing future posts on the solutions you've been able to come up with!

Yeah, I got a serious education doing this for mail items. And I had it easier as I was able to control the background and lighting and camera and everything.

Well, I couldn't control the autofocus very well, going from a $500 DSLR to a $1200 DSLR made HUGE gains since it'd have far, far more autofocus points.

I was really interested in the text output of the OCR that I later did (which was a treat in itself since mail has so many different fonts, even on the same item!). I learned a lot about a lot of things too.

For the 3D deformation take a look at this part of OpenCV:

http://docs.opencv.org/2.4/doc/tutorials/features2d/feature_...

Reply on news.ycombinator.com