  While vehicle license plate recognition (VLPR) is usually done with a sliding
window approach, it can have limited performance on datasets with characters
that are of variable width. This can be solved by hand-crafting algorithms to
prescale the characters. While this approach can work fairly well, the
recognizer is only aware of the pixels within each detector window, and fails
to account for other contextual information that might be present in other
parts of the image. A sliding window approach also requires training data in
the form of presegmented characters, which can be more difficult to obtain. In
this paper, we propose a unified ConvNet-RNN model to recognize real-world
captured license plate photographs. By using a Convolutional Neural Network
(ConvNet) to perform feature extraction and using a Recurrent Neural Network
(RNN) for sequencing, we address the problem of sliding window approaches being
unable to access the context of the entire image by feeding the entire image as
input to the ConvNet. This has the added benefit of being able to perform
end-to-end training of the entire model on labelled, full license plate images.
Experimental results comparing the ConvNet-RNN architecture to a sliding
window-based approach shows that the ConvNet-RNN architecture performs
significantly better.
