Automatic-Car-License-Number-Plate-Detection-Recognition;
->Car number plate detection is a technology-driven solution that involves the automatic identification and recognition of vehicle number plates using image processing and computer vision techniques. This use case demonstrates how car number plate detection can be employed in various real-world scenarios to enhance safety, security, and efficiency.
->Introduction: The Automatic Car License Number Plate Detection and Recognition system is an essential component of modern intelligent transportation systems. It provides a robust solution for accurately identifying and extracting license plate information from vehicle images or videos. In this proposal, we outline the use of the Inception model, a deep learning architecture known for its excellent performance in image recognition tasks, for license plate detection and recognition.
->Objective: The primary objective of this project is to develop a reliable and efficient system that can automatically detect and recognize license plates from car images or videos. The proposed solution will leverage the power of the Inception model to achieve high accuracy and robustness in license plate identification.
Proposed Solution:
- Dataset Preparation:
Collect a diverse dataset of car images or videos with labeled license plate regions. Annotate the license plate regions in the images or videos to create ground truth data. Augment the dataset by applying transformations like rotation, scaling, and blurring to improve the model's generalization ability.
- Model Training:
Utilize the Inception model, a convolutional neural network architecture that has shown excellent performance in image recognition tasks. Fine-tune the Inception model using the annotated license plate dataset. Train the model on a GPU-accelerated machine to expedite the training process. Optimize hyperparameters such as learning rate, batch size, and regularization techniques to achieve the best performance.
- License Plate Detection:
Apply the trained Inception model to input images or video frames. Utilize techniques such as sliding windows or selective search to localize potential license plate regions. Employ non-maximum suppression to eliminate duplicate or overlapping detections. Extract the detected license plate regions for further processing.
- License Plate Recognition:
Perform pre-processing on the extracted license plate regions, including resizing, normalization, and noise reduction. Employ optical character recognition (OCR) techniques to recognize the characters on the license plate. Apply post-processing steps to refine the recognized characters, such as spell-checking and context-based corrections. Output the recognized license plate number or alphanumeric code.
5.System Integration:
Develop a user-friendly interface for inputting car images or videos. Implement the license plate detection and recognition algorithms using a programming language (e.g., Python) and relevant libraries (e.g., TensorFlow, OpenCV). Integrate the system with a database or storage mechanism to store the recognized license plate numbers along with the corresponding timestamps and other relevant information.
Research based Project Summary; By leveraging the power of the Inception model, we propose a solution for automatic car license number plate detection and recognition. This system will significantly contribute to various applications, such as traffic monitoring, parking management, law enforcement, and toll collection. The proposed solution combines the strengths of deep learning, computer vision techniques, and OCR algorithms.
->Sample Predicted output of the test Datasets;
Dataset Available; ->https://www.kaggle.com/datasets/aslanahmedov/number-plate-detection ->https://www.kaggle.com/datasets/adityadesai13/used-car-dataset-ford-and-mercedes