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Page Dewarping (mzucker.github.io)
743 points by pietrofmaggi on Aug 18, 2016 | hide | past | web | favorite | 70 comments



This strikes close to home for me, because I've done this "by hand" quite a number of times, using some interactive Gimp filters, with very good results. I've been able to take a perspective photo of a curved page lying on a desk, get it almost perfectly straight, and threshold it to clean black and white or sharp gray scale.

Here is my very quick and dirty manual job of the same example page:

http://www.kylheku.com/~kaz/dewarp.png

Literally less than five minutes.

First I cropped the image. Then duplicated the layer. Blurred the top layer (Gaussian, 50 radius). Then flipped to Divide mode and merged the visible layers. This leveled the lightness quite well, almost completely eliminating the shadow over he right side of the page and all other lighting differences. There is a hint of the edge of the shadow still present because it is such a sharp contrast; but that can be eliminated in an adjustment of the intensity curves. In such cases it may be helpful to experiment with smaller blur radii, too.

I then did a perspective transform in the lateral direction, squeezing the left side top-bottom and expanding the right, resulting in the warp now being approximately horizontal. (The perspective transform is not just for adding a perspective effect; it is also useful reversing perspective!)

Finally, I used the Curve Bend (with its horrible interactive interface and awful preview) to warp in a compensating way. Basically, the idea is to draw an upper and lower curve which is the opposite of the curve on the page. I made two attempts, keeping the results of the second.

If the preview of this tool wasn't a ridiculous, inscrutable thumbnail, it would be possible to do an excellent job in one attempt, probably close to perfect.

Because the page is evenly light thanks to the divide-by-blurred layer trick, it will nicely threshold to black and white, or a narrow grayscale range.


Hence the tagline on the blog: "Why do it by hand if you can code it in just quadruple the time?" :)


More work up front, less work afterwards.


I really enjoyed the various random thoughts in the wrapping up section of this post, thank you a good read!


Divide by blur is very crafty. Will have to give that a shot next time I have a similar need in Photoshop.


As a comic artist working with scanned images, the "divide by blur" trick is going to save me significant effort in the future.

THIS is exactly the sort of insight I wish I would get when I click one of those "one weird trick" links!


Then you might also like the Grain Extract and Grain Merge layer modes in GIMP (not sure if they exist in photoshop).

They're a bit obscure if you don't know how they work, basically Grain Extract subtracts the layers and adds 128, so you get sort of a fake 8-bit signed integer. Grain Merge does the opposite (add two layers, subtract 128).

I haven't tried to divide-by-blur (but I'm going to :) ). Grain Extract on the other hand, allows to subtract-with-blur, which is more like what I'd do if I were to code such an algorithm myself (the operation is roughly what the Unsharp Mask filter does, but you get a bit more control). Still I'm curious to see how divide-by-blur differs in its results.

Check this Flickr-post which has a nice explanation of using Grain Extract (used for a different purpose here, it's really a very interesting and versatile trick, only discovered it last week myself):

https://www.flickr.com/groups/48637250@N00/discuss/721576192...


I should mention that I know about Grain Extract's subtraction operation. I've used it for "leveling" also.

Subtraction would be fine if the data were logarithmic: that is to say, if the intensity information we are operating on represents decibels. We figure out the low frequency signal's "floor" over each area of the image, and simply subtract those decibels.

Division is better on the assumption that the intensity values of the pixels are linear. Division of linear samples is subtraction, in the decibel scale.

The right way is somewhere in between, because in RGB displays, pixels are put through some gamma curve. They are neither linear nor logarithmic.


Reminds me of the "frequency separation" [1] technique, used a lot in the high end retouching business.

[1] https://fstoppers.com/post-production/ultimate-guide-frequen...


Echoing what the other comment said, blurring and setting as Divide has never occurred to me. I just tried it and it does a very good job at leveling. Nice one.


The radius of 50 was too large. Reason being: I'm used to working with much higher resolution scans, and without high contrast shadows across them. A blur radius of around 15 does a good job of eliminating the shadow edge in this image after the divide. What we're basically doing is a high pass filter. The radius of 50 is too low a frequency; it lets through the shadow edge noticeably.

By the way, here is a useful trick: blurred layer in Divide mode, plus vary the opacity. You can reduce the amount of contrast between light and dark areas in an image without completely leveling it. Works well with low opacity levels (just a slight blend of the Divide mode layer). If you have a picture with areas that are too dark and too bright, a touch of this can help. High radii tend to work well, with blends of up to 20% or so.

For me, this does what I want Retinex to do! Only better, with intuitive control parameters and predictable results.


Just came up with an improvement to the above: Divide by Blur, with 0-20% blend. However, not over the RGB image, but only over its its L (lightness) channel in a LAB colorspace separation. Then, recompose. That looks really natural for brightening the shadows and reducing highlights.

Look at the picture of the little girl peering out of the back window of a truck on NASA's Retinex page, as enhanced by Retinex:

http://dragon.larc.nasa.gov/retinex/pao/news/

Now my version:

http://www.kylheku.com/~kaz/nasa-image-girl-truck.png

The reflection in the glass is brought out in a less cheesy way that you might not guess is the result of processing, if you don't already know.

I did a decompose of the image to the LAB color space (Colors/Components/Decompose...). Then I used a blur radius of 200 pixels on a copy of the L layer. Put into Divide mode and blended at a bit over 30%, and recomposed from LAB back to the original RGB image.

(That's a simplification; of course I had to struggle with Gimp to preserve the ID of the L layer, which is destroyed by a straight "merge layer down" after which the recompose operation fails with an error. I in fact made two copies of the L layer, and did the processing and merge operation between those two copies. Then I did a select all to copy the resulting layer to the clipboard and pasted that into the original L layer to replace it.)


I love well-illustrated writeups. Even a reader without mathematics or programming knowledge can understand what steps the author took. His model actually seems to better represent the warped paper than a cylinder would. (though I don't know the actual specifics of the CTM model)

I wish he went into more details on the steps taken after dewarping. You can tweak the image levels to get good contrast, but surprisingly there aren't any shadows from underleveling or loss of detail from overleveling. I wonder if the author ran OCR on the scans after, and speaking of OCR, IIRC Leptonica is one of the dependencies of Tesseract so it must do some similar pre-processing.

Edit: reading more carefully, he mentions that he used adaptive thresholding from OpenCV.


Recently, Dropbox wrote about dewarping prior to OCR in their app: https://news.ycombinator.com/item?id=12297944

This code had the same idea, and is open-source!


No, what this code does is much more sophisticated - Dropbox do not dewarp (i.e. remove non-linear distortions) they only transform the image to make the document rectilinear (leaving the distortions/deformations intact), which is much simpler.


Dewarping images for OCR isn't a new idea at all - while older systems used to do simple de-skew (tilting the image) for speed reasons, the need for warping the image to improve results has been known for a very long time, and most production quality OCR engines has done something like it for a long time.

(this isn't to slight the article - it's a great, well written presentation on how to implement it)


I use Microsoft's "Office Lens" app on my Android phone all the time, like a "smart camera" which automatically squares off and white-balances each photo of a page (usually mail or forms filled in by hand). It can't handle warped pages though, so I hope they add something like this!


Yes, Office Lens is great for that. I recently switched to CamScanner, which has an even better algorithm for deskewing pages (take a pic of a receipt at an odd angle, it automatically transforms it to flat and rectangular). Pro version was on sale -- I'm not affiliated, just a happy customer and highly related to this article.


I have it as well, but use Office Lens or Google Drive (create a shortcut to a folder on home screen of android device and use it to scan into it).

The reason is CamScanner tried to upsell me to some monthly plan.

For all good advice on HN that you should build recurring revenue: It seriously annoys me when people tries to do that by demanding monthly payments for static features.

(Totally OK with selling license keys for new features etc.)


Update: CamScanner is now less annoying than what I used to remember. I might actually be switching back.


it came in handy whenever a student emailed me their homework as a pile of JPEGs.

Gotta admire this guy's resourcefulness—and patience. If I were a professor, I'd probably just reject the assignment outright if a student sent me a bunch of photos from their smartphone in lieu of a PDF or a "proper" scan. :)


Or compromise; write up an awesome blog post about how you wrote software to turn their photos into proper scans, and reject their homework (with a link to the blog post).


Reject it, in the form of a picture of a handwritten note on a print-out of his jpegs.


Thanks for the feedback, everyone -- happy to answer questions here or in the Disqus comments on my blog.


This is really interesting, thanks!

If you decide to try to make this faster, check out ceres[0] a non-linear least squares optimisation framework that does automatic differentiation using a clever C++ template hack.

I've used it a few times to solve these kind of problems and found it to be very good!

[0] http://ceres-solver.org/


Yep, I'm still waiting to use ceres for something - I didn't end up using it on my image approximation project https://mzucker.github.io/2016/08/01/gabor-2.html because it doesn't work well with inequality constraints.


Ah yes, only supporting constraints on the parameters can be annoying if your problem requires that.


I really enjoyed the article!

By the way, you mentioned using word boundaries in regexes to replace variable names. GNU Emacs regexes can actually include "symbol boundaries" (which are a little better for variable names than word boundaries), represented as "\_<symbol\_>". Personally, I like using the "highlight-symbol" package, which provides the "highlight-symbol-query-replace" command to basically execute M-% for the symbol at point.


Cool - will check it out!


Here's something that I think has not been done, but could be quite lucrative, building a high resolution scanner using the phone camera, multiple pictures and interpolation/noise removal.

Most phone cameras these days have good resolutions, and you could technically take a 6x4 photo, divvy it to 3x3 grid and take close up photos, and have smart algorithms interpolate the pixels to form a single image with high res. I'd even bet you'd results equal to or better than a flat bed scanner.

For better us, just open the camera preview and slowly pan over the image.

Has someone tried something like this? With FOSS apps like mosaic, hdr tools and imagemagick, it should be possible. I'm guessing opencv would be needed for interpolation and noise removal..


I've used some free apps that turn phones into document scanners. Almost all banks I use have something like that embedded for check deposits. Maybe they're just doing deskew rather than dewarping though... loose documents aren't usually warped like book pages.

The next hard problem would be help with DIY book scanning. Like the camera could sit over my shoulder and detect when I've turned a page, then automatically take a picture of the new page. Then OCR kicks in, and conversion to EPUB, preserving graphics when pages have non-text elements. Mostly just feel like we can probably do better than the massive contraptions over at www.diybookscanner.org


This problem is fundamentally different with stereo images; there is a hope of reconstructing the exact 3D geometry of the page before flattening, rather than inferring from content. An iPhone app that did this would do well.


I've always thought it should be possible using a short video of the camera orbiting the page from different angles. (Assuming a static scene of course.)


Or now, some frames of a live photo.


That sounds like a good idea and pretty straightforward to implement -- now I almost wanna try it...


I wonder if someone has tried to dewarp whole book from a video when flipping through it. I imagine that could be handy way to copy the whole book.


Page warp becomes but a tiny part of the problem with a page-flip video – now you have motion blur, varying exposure, resolution, noise, and depth of field to worry about. Maybe if you had a camera capable of recording extremely high frame rates at high resolution (probably north of 120fps @ 1440p), a high-intensity, diffuse light source, and a lens with a tight aperture / narrow focal length, you’d at least have something to work with.


Doesn't that describe most modern cell phone cameras?


I haven't heard of a phone camera that can do video at 120fps, they're usually 30, maybe 60 fps. The S7 has some high framerate slow-mo option, but it's limited to a 720p resolution.


The iPhone 6S records 1080p at 120 fps and 720p at 240 fps for their Slow-Mo feature [0].

[0]: http://www.apple.com/ca/iphone-6s/specs/


The iphone 6S can do 120 fps for a short period of time.



This is great! My wife is a music teacher and often scans sheet music so that it's more portable. She has been asking me for a while for something exactly like this. I'll have to tweak it to work on sheet music, since I imagine his methods to identify lines of text won't work for the music staff out of the box.


ScanTailor [1] also implements automatic dewarping and is overall great for scan postprocessing.

[1] http://scantailor.org/


I used it for about 1000 pages I photographed with a DSLR. The automatic dewarping rarely worked for me, so I ended up doing it manually.

It likely didn't help that many of the pages were typed carbon copies or hand-written. For the former I had to put another piece of paper behind the page to prevent the next page from bleeding through.

That said, I managed to get the job done, though it took a couple of weeks. Next is to capture the metadata (author, date written, ...). There were a lot of one page letters in the documents I copied.

Would love to try out another tool that could read from the ScanTailor project file to get the page segmentation or even the warping I did manually to improve on the result.


He mentions adding line detection for tables, which should be a good start for sheet music.


Yep, might just work out of the box -- give it a shot!


If she literally scans it with a scanner, why would it need dewarping? This is for when you use a digital camera on pages not lying flat.

If you have the option go for scanning.

Scans still benefit from cleanup, such as rotation and adjusting the levels (making everything near white perfectly white) and possibly manual cleanup (removal of specks of dust or the odd hair that was on the scanner).


She scans it in a scanner, but the music is often in large books that don't lay completely flat, so you still see the warping near the binding.


I built one of these, it's really simple and quick to make. http://www.instructables.com/id/Bargain-Price-Book-Scanner-F...

They still warp, but less than laying flat on the table and it's quicker if you do all the even pages, then all the odd pages. Actually the hardest part is making the camera stable so it doesn't move when you press the button.

Lots of info on different hardware setups and scanning software here that can batch translate into pdf and different formats.

http://www.diybookscanner.org/forum/index.php


With that setup, I'd just find a sheet of glass to press the page flat. Cameras have resolution in the right ball park these days. 600 dpi across 8 inches is, what, 4800 pixels.


I remember when I was in my middle school orchestra, occasionally I'd get a piece of sheet music with this problem. If it was bad enough, it might even make it impossible to read the last few notes. If you could fix this for your wife, I'm sure her students will greatly appreciate it!


Or, someone might play those last few notes ritardando, since they are bunching together. :)


Ah that; it's a major difficulty. A problem is that not only does the shape warp, but any part of the image that is lifted away from the scanner glass, even slightly, gets blurred quite a bit.


You could also try ScanTailor: http://scantailor.org/


Thanks for the suggestion! I'll check it out.


The next step would be to "depixelate" the resulting image. How could this be done? I guess OCR would not work because of the variation of the fonts (you don't want the document to end up in a single font; you want to keep the fonts). Could a deep learning approach work here, even if it has not been trained on all the specific fonts?


Plenty of engines will do OCR and use the shapes recognised with high certainty to affect how they detect the rest.

There are many ways of doing this, and you can achieve some results even without knowing if your image is text, but just has lots of self-similarity by virtually sliding a "grid" over the image, slicing it up into n-by-n squares, running any of a number of nearest-neighbour variants over it, and then for each cluster replace all instances of the squares in the cluster by the one which minimise the overall error rate vs the others.

This will work reasonably well for very structured images such as text, as long as enough characters are near correct, and will retain custom fonts etc. but clean them up quite a bit as long as they either are different enough, or occur often enough on a page to not get "corrected".

I'm sure there are better ways of doing this too - it's been a decade since I kept up with OCR research.


How much of an effect does the camera lens make in page warping? Correct me if I'm wrong, but for shorter focal length lenses I would think it would warp the page more. If a person accounted for that, could they get a near perfect result? Or does his algorithm account for that? It seems that one would need to know where the center of the image would be.


Pretty sure focal length would affect things as you say, but also physical dimensions matter too. My program assumes fixed focal length and then picks the page dimensions that work with the assumed focal length -- almost certainly not the correct ones.


"You can see these are not exactly small optimization problems. The smallest one has 89 parameters in the model, and the largest has 600."

Those are small optmization problems. These types of problems are solved in computer vision for hundreds of thousands of variables. His problem can be solved in real-time, not tens of seconds.


Compared to weather prediction, which is an optimisation problem involving hundreds of millions of variables, and occupies a hefty supercomputing cluster for a few hours.


Consider mentioning Dan Bloomberg as the author of the original work as well as Leptonica. :)


Thank you for the gif near the start - it really helped me to understand what was going on.


Is using a curve whose end points are fixed to zero to model the warping accurate? I can't see a rationale for why the end points should both be 0.


The main rationale is removing redundant degrees of freedom -- since the page is allowed to rotate freely, the edges of the page can still move around plenty.


I see, so basically assume the two end points are at zero and there is some rotation accounting for the endpoint offset in real space.

It still doesn't seem fully accurate as I can imagine a non-rotated cubic curve with endpoints at an offset, but I assume your simplification works well enough.


I've always been interested in getting into graphics programming, and stuff like this only makes me more interested. Really well written post.


Wrap this in a service and you have a startup!


This is solid. I'm an engineer at Algorithmia, and this caught our attention as the sort of project we love to host as a service on our algorithm marketplace. We've already made note of it for our team to consider adding (thanks to the generous MIT license), but I wanted to reach out in case you'd rather add, own, and optionally monetize it on our platform yourself. Either way, this was a great read with impressive results.




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