[Chicago] OpenALPR - Transformations

[Michael Tamillow]

Hi Chris,

http://www.ilga.gov/legislation/BillStatus.asp?DocNum=1351&GAID=13&DocTypeID=SB&LegId=87838&SessionID=88

It looks like what you are doing is likely not legal in the state of Illinois (or soon won't be). I'm not a lawyer, and I don't know the exact nature of your project, nor do I know the exact status of the act, so I can't say anything about that fine line. Probably best to avoid anything that amounts to collecting personal data on strangers that could be used to identify them. And if you are knowingly breaking the law, better not to create a virtual trail (e.g. Paul Combetta). If this is simply a learning experience for image recognition, may I suggest the MNIST data set for starters.

Sent from my iPhone

> On Nov 4, 2016, at 10:14 PM, Jason Wirth <wirth.jason at gmail.com> wrote:
> 
> Could you be a little more specific in the problem you have; I looked at the sample image on the site and tried a couple random images on the demo and it detected a skewed plate so I'm not sure what's not working. Is it that yours is highly skewed while theirs only detects moderately skewed images, or it cannot read the numbers correctly? 
> 
> Also, what do you mean by "sometimes that it doesn't read it correctly"? What's the overall accuracy? 
> 
> In many cases you can automatically create a huge training data set by taking a good image then applying various tilt, skew, and reshaping transformations. Perhaps you can train something yourself.
> 
> 
> 
> 
> 
> 
> -- 
>     Jason Wirth
>     wirth.jason at gmail.com
> 
>> On Fri, Nov 4, 2016 at 2:38 PM, Robare, Phillip (TEKSystems) <proba at allstate.com> wrote:
>> Some links you may find useful below.  I am surprised ALPR doesn't do this already.
>> 
>> The problems reading license plates by humans have resulted in deaths (due to hits on mis-entered license numbers in the context of traffic stops).  I hope you are getting a better feeling of how we need to be careful with the tasks given to our new robotic overlords.
>> 
>> An interesting post from a couple years ago is "How to Build a Kick-Ass Mobile Document Scanner in Just 5 Minutes" (http://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/) .  It covers sharpening the image, doing a perspective transformation and getting the document ready for OCR (but not the OCR step).
>> 
>> The book "Automate The Boring Stuff With Python" has a chapter on "Manipulating Images" that is online and covers how to do the manipulations with PIL (https://automatetheboringstuff.com/chapter17/).  This would be a good start.  If you are working on your project with Python 3 (if not, why not?) I believe Pillow has the same API.
>> 
>> There have been a number of github projects in the space of preparing scanned documents that I have seen over the years.  Unfortunately I can't find the best ones I remember so here are ones I found today:
>> Scantailor (https://github.com/scantailor/scantailor) is a large C++ project that appears to be very complete.  ImproveQuality (https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuality) is also a cpp project meant for text scanning.
>> 
>> A mathematically interesting approach to deskewing is documented in http://www.ijstr.org/final-print/dec2013/An-Integrated-Skew-Detection-And-Correction-Using-Fast-Fourier-Transform-And-Dct.pdf where a Fast Fourier Transform is used to determine the skew without pre-analyzing the image to pull out text lines.
>> 
>> 
>> Phil Robare
>> 
>> 
>> -----Original Message-----
>> From: Chicago [mailto:chicago-bounces+proba=allstate.com at python.org] On Behalf Of Chris Vinzons
>> Sent: Thursday, November 03, 2016 2:52 PM
>> To: chicago at python.org
>> Subject: [Chicago] OpenALPR - Transformations
>> 
>> Hi,
>> 
>> I'm using OpenALPR to read still license plates, but the thing is that my sometimes that it doesn't read it correctly. I think this is because of the license plate is tilted. Is there a way to untilt it or is there some kind of training data I could do in python?
>> 
>> 
>> Thanks
>> 
>> Chris V
>> 
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