A Web-based Multimodal Vehicle Plate Recognition System

In the current project, a basic and feasible multimodal vehicle plate recognition system is designed using web technologies combined with Python-based processing. The designed system is capable of handling image, audio, and video inputs, thus facilitating overall vehicle analysis through a single web interface. Number plate recognition from images is done through computer vision and optical character recognition, whereas audio and video inputs are processed to obtain additional vehicle-related information. Experimental analysis proves that the image modality has an accuracy of 92%, followed by video processing with an accuracy of 88%, and audio analysis with an accuracy of 85%, thus proving the feasibility of multimodal fusion. The system is capable of image capture, preprocessing, segmentation, and recognition efficiently using open-source technologies, thus ensuring scalability and ease of implementation. The proposed method emphasizes the feasibility of multiple data modalities for improved vehicle analysis and intelligent transportation systems.