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While vehicle license plate recognition (VLPR) is usually done with a slidingwindow approach, it can have limited performance on datasets with charactersthat are of variable width. This can be solved by hand-crafting algorithms toprescale the characters. While this approach can work fairly well, therecognizer is only aware of the pixels within each detector window, and failsto account for other contextual information that might be present in otherparts of the image. A sliding window approach also requires training data inthe form of presegmented characters, which can be more difficult to obtain. Inthis paper, we propose a unified ConvNet-RNN model to recognize real-worldcaptured 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 beingunable to access the context of the entire image by feeding the entire image asinput to the ConvNet. This has the added benefit of being able to performend-to-end training of the entire model on labelled, full license plate images.Experimental results comparing the ConvNet-RNN architecture to a slidingwindow-based approach shows that the ConvNet-RNN architecture performssignificantly better.

Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN