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The development of the Automatic License Plate Recognition (ALPR) system hasreceived much attention for the English license plate. However, despite beingthe sixth largest population around the world, no significant progress can betracked in the Bengali language countries or states for the ALPR systemaddressing their more alarming traffic management with inadequate road-safetymeasures. This paper reports a computationally efficient and reasonablyaccurate Automatic License Plate Recognition (ALPR) system for Bengalicharacters with a new end-to-end DNN model that we call Bengali License PlateNetwork(BLPnet). The cascaded architecture for detecting vehicle regions priorto vehicle license plate (VLP) in the model is proposed to eliminate falsepositives resulting in higher detection accuracy of VLP. Besides, a lower setof trainable parameters is considered for reducing the computational costmaking the system faster and more compatible for a real-time application. Witha Computational Neural Network (CNN)based new Bengali OCR engine andword-mapping process, the model is characters rotation invariant, and canreadily extract, detect and output the complete license plate number of avehicle. The model feeding with17 frames per second (fps) on real-time videofootage can detect a vehicle with the Mean Squared Error (MSE) of 0.0152, andthe mean license plate character recognition accuracy of 95%. While compared tothe other models, an improvement of 5% and 20% were recorded for the BLPnetoverthe prominent YOLO-based ALPR model and the Tesseract model for thenumber-plate detection accuracy and time requirement, respectively.

BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition