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Created by on May 06, 2016. Introduction Over the past few weeks I’ve been dabbling with deep learning, in particular. One standout paper from recent times is Google’s. This paper describes a system for extracting house numbers from street view imagery using a single end-to-end neural network.

Automatic license plate recognition. OmegaT is a free and open source multiplatform Computer. Simple and Effective Source Code For Write Recognition. Download source code for How to implement automated license plate recognition in C# using OCR This documentation is built up from two major parts: the first part consists of a rather theoretical and general description of license plate recognition and the Computer Vision technology in order to implement the example successfully; while the second part is a more detailed and programming- oriented documentation.

The authors then go on to explain how the same network can be applied to breaking Google’s own CAPTCHA system with human-level accuracy. In order to get some hands-on experience with implementing neural networks I decided I’d design a system to solve a similar problem: Automated number plate recognition (automated license plate recognition if you’re in the US). My reasons for doing this are three-fold: • I should be able to use the same (or a similar) network architecture as the Google paper: The Google architecture was shown to work equally well at solving CAPTCHAs, as such it’s reasonable to assume that it’d perform well on reading number plates too. Having a known good network architecture will greatly simplify things as I learn the ropes of CNNs.

Disneyland Paris Rct2 Downloads. • I can easily generate training data. One of the major issues with training neural networks is the requirement for lots of labelled training data. Hundreds of thousands of labelled training images are often required to properly train a network. Fortunately, the relevant uniformity of UK number plates means I can synthesize training data. Traditional ANPR systems hand-written algorithms for plate localization, normalization, segmentation, character recognition etc. As such these systems tend to be many thousands of lines long.

It’d be interesting to see how good a system I can develop with minimal domain-specific knowledge with a relatively small amount of code. For this project I’ve used Python,, and. Inputs, outputs and windowing In order to simplify generating training images and to reduce computational requirements I decided my network would operate on 128x64 grayscale input images.

128x64 was chosen as the input resolution as this is small enough to permit training in a reasonable amount of time with modest resources, but also large enough for number plates to be somewhat readable: In order to detect number plates in larger images a sliding window approach is used at various scales: The image on the right is the 128x64 input that the neural net sees, whereas the left shows the window in the context of the original input image. For each window the network should output: • The probability a number plate is present in the input image.

(Shown as a green box in the above animation). • The probability of the digit in each position, ie. For each of the 7 possible positions it should return a probability distribution across the 36 possible characters.

(For this project I assume number plates have exactly 7 characters, as is the case with most UK number plates). A plate is considered present if and only if: • The plate falls entirely within the image bounds. • The plate’s width is less than 80% of the image’s width, and the plate’s height is less than 87.5% of the image’s height. • The plate’s width is greater than 60% of the image’s width or the plate’s height is greater than 60% of the image’s height. With these numbers we can use a sliding window that moves 8 pixels at a time, and zooms in times between zoom levels and be guaranteed not to miss any plates, while at the same time not generating an excessive number of matches for any single plate. Any duplicates that do occur are combined in a post-processing step (explained later). Synthesizing images To train any neural net a set of training data along with correct outputs must be provided.