ANPR contracts with local and federal governments tend to be highly competitive.ANPR datasets are tedious to curate, requiring an incredible investment of time and staff hours to annotate.These datasets contain sensitive identifying information related to the vehicle, driver, and location.Several compounding factors make ANPR incredibly challenging, including finding a dataset you can use to train a custom ANPR model! Large, robust ANPR datasets that are used to train state-of-the-art models are closely guarded and rarely (if ever) released publicly: It needs to be able to detect the license plate of each car passing by, OCR the characters on the plate, and then store this information in a database so the owner of the vehicle can be billed for the toll.
#Xeoma license plate recognition sensitivity software#
State-of-the-art ANPR software utilizes Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) to aid in better OCR’ing of the text from the license plates themselves.More advanced ANPR systems utilize dedicated object detectors, such as HOG + Linear SVM, Faster R-CNN, SSDs, and YOLO, to localize license plates in images.ANPR performed in controlled lighting conditions with predictable license plate types can use basic image processing techniques.If you enjoy the tutorial, you should definitely take a look at the book for more OCR educational content and case studies!Īutomatic License/Number Plate Recognition systems come in all shapes and sizes: I’ve covered it in detail inside the PyImageSearch Gurus course, and this blog post also appears as a chapter in my upcoming Optical Character Recognition book.
ANPR is one of the most requested topics here on the PyImageSearch blog.
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