For my final project at university, I'm developing a vehicle license plate detection application. I consider myself an intermediate programmer, however my mathematics knowledge lacks anything above secondary school, which makes producing the right formulas harder than it probably should be.
I've spend a good amount of time looking up academic papers such as:
When it comes to the math, I'm lost. Due to this testing various graphic images proved productive, for example:
to
However this approach only worked to that particular image, and if the techniques were applied to different images, I'm sure a poorer conversion would occur. I've read about a formula called the "bottom hat morphology transform", which does the following:
Basically, the trans- formation keeps all the dark details of the picture, and eliminates everything else (including bigger dark regions and light regions).
I can't find much information on this, however the image within the documentation near the end of the report shows its effectiveness.
I need advice on what transformation techniques I should focus on developing, and what algorithms can help me.
EDIT: New information present on Continued - Vehicle License Plate Detection
There are a number of approaches you can take but the first strategy that pops into mind is to:
Like I said, this is one strategy of many but it comes to mind as one requiring the least amount of heavy math... that is if you can find an OCR implementation that will work for you.
I did a similar project a few years ago in Java, first I applied the Sobel operator and then masked all the image with an image of a plate (with the Sobel operator applied too). The region of maximum coincidence is where the plate is. Then apply an OCR to the selected region to get the number.
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