LPR CNN Cascade and Adaptive Deskewing

License Plate Recognition (LPR) is a well-known image processing technology. The goal of this paper is to achieve the same or better accuracy than previous known algorithms while attaining a higher speed of processing and a modular yet simple approach. The engine is mainly designed to work on Lebanese license plates but can easily be trained for others. The results below are obtained by using a deep convolutional neural network cascade for classification (CNN cascade), a CNN with partially connected deep layers for deskewing and a neural network optimized by neuro-evolution for OCR. This resulted in a modular LPR solution that surpasses the conventional solution in terms of speed and accuracy, a deskew module that can straighten double lined plates with far better accuracy than its image processing counterpart and an OCR module that's optimized for the best speed and accuracy achievable even predicting characters that are unreadable by conventional solutions. On top of that, the whole solution is GPU (Graphical Processing Unit) enabled making it scalable for a large network of cameras with a central processing unit.