Contents. System Requirement Component Requirement Detail Emgu CV Operation System Windows Only The.net wrapper to the OCR engine is written in managed C It is NOT compatible with Linux License Plate Recognition According to Automatic number plate recognition (ANPR; see also other names below) is a mass surveillance method that uses optical character recognition on images to read the license plates on vehicles.
As of 2006, systems can scan number plates at around one per second on cars traveling up to 100 mph (160 km/h).citation needed They can use existing closed-circuit television or road-rule enforcement cameras, or ones specifically designed for the task. They are used by various police forces and as a method of electronic toll collection on pay-per-use roads and monitoring traffic activity, such as red light adherence in an intersection. ANPR can be used to store the images captured by the cameras as well as the text from the license plate, with some configurable to store a photograph of the driver.
Systems commonly use infrared lighting to allow the camera to take the picture at any time of the day. A powerful flash is included in at least one version of the intersection-monitoring cameras, serving both to illuminate the picture and to make the offender aware of his or her mistake. ANPR technology tends to be region-specific, owing to plate variation from place to place.
This tutorial's approach to ANPR is divided into two stage. In the first stage, we perform license plate region detection. In the second stage, we perform OCR on the license plate to recover the license number Assumption This tutorial assumes that ANPR is performed on European license plate. Within the source code, you will find the following lines of code that indicates only rectangle with width-height ratio in the range of (3.0, 8.0) is considered. Double whRatio = ( double )box. Height; if (!
Background 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.
License Plate Recognition Source Code C ++ Calculator
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. Other constraints. Developing in C#.
Confining the project to UK registration plates only. I can choose the images to convert as a demonstration Question I need advice on what transformation techniques I should focus on developing, and what algorithms can help me. EDIT: New information present on. There are a number of approaches you can take but the first strategy that pops into mind is to:. Discovery/research: Identify the set of colors and fonts that you may need to identify.
If your sample picture is representative of most British plates then your job is made easier. Simple, singular font and black lettering on a white background. Code: Attempt to identify a rectangular region of an image where the colors are predominantly white and black.
This is not a terribly math-heavy problem and it should give you the license plate region to concentrate on. Code: Do some clean up on your subregion such conversion to pure black and white (monochrome) and perhaps scaling/shifting into a nice, tight rectangle.
Use API: Next employ an existing OCR (optical character recognition) algorithm on your sub-selected image region so see if you can read the text. 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. Here's how I suggest you should do this task. Read my detailed answer. Convert to Grayscale.
Gaussian Blur with 3x3 or 5x5 filter. Apply Sobel Filter to find vertical edges.
Sobel(gray, dst, -1, 1, 0). Threshold the resultant image to get a binary image.
Apply a morphological close operation using suitable structuring element. Find contours of the resulting image. Find minAreaRect of each contour. Select rectangles based on aspect ratio and minimum and maximum area. For each selected contour, find edge density.
Set a threshold for edge density and choose the rectangles breaching that threshold as possible plate regions. Few rectangles will remain after this. You can filter them based on orientation or any criteria you deem suitable. Clip these detected rectangular portions from an image after adaptiveThreshold of the original (grayscale) image and apply OCR.
You can also refer to &. This will also give you some idea about how to approach things,and how existing solutions are.
But as answered by paul, you should first try to find the rectangular number plate from the complete image and then binarize it and then use the OCR libraries available (Tesseract would be recommended) You can refer to this which will help you find the rectangular plate. You need to use openCV libraries, so you will not need alot of math but yes a basic understanding of what is happening behind the scenes can help you solve the problem in better way.
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Insults are not welcome. Don't tell someone to read the manual. Chances are they have and don't get it. Provide an answer or move on to the next question. Let's work to help developers, not make them feel stupid.