Algorithm for License Plate Recognition Using BP Neural Network

Application of BP Neural Network in License Plate Recognition

License plate recognition, as one of the core technologies in intelligent transportation systems, primarily involves accurately locating and identifying character information from images with complex backgrounds. The Backpropagation (BP) Neural Network, with its powerful nonlinear mapping capabilities and adaptive learning characteristics, has proven to be an effective tool for addressing this challenge.

Algorithm Implementation Approach Preprocessing Stage License plate recognition first requires image preprocessing operations including grayscale conversion, binarization, and noise reduction to eliminate uneven illumination and background interference. Subsequently, license plate regions are located through edge detection or color space-based methods, followed by segmentation of individual characters.

Feature Extraction Segmented character images are normalized to uniform dimensions, with key features (such as pixel distribution, edge gradients, or projection features) extracted as input for the neural network. Traditional methods might employ manually designed features, while deep learning integration enables automatic feature extraction through convolutional layers (e.g., using OpenCV for traditional features or TensorFlow/PyTorch for CNN-based extraction).

BP Neural Network Design Input Layer: Number of nodes corresponds to feature vector dimensionality (e.g., 20×20 image flattened into 400 dimensions). Hidden Layers: Typically 1-2 layers, with node numbers optimized through experimentation (e.g., 128 nodes per layer). Activation functions commonly use Sigmoid or ReLU implementations. Output Layer: Node count matches character categories (e.g., 65 classes for Chinese characters + digits + letters), employing Softmax function for probability distribution output.

Training and Optimization Using annotated character sample datasets, weights are adjusted through backpropagation algorithm. Key techniques include: Dynamic Learning Rate Adjustment: Implemented via Adam or RMSprop optimizers to prevent oscillations or slow convergence. Regularization: Such as L2 penalty terms to avoid overfitting. Early Stopping: Training termination based on validation set accuracy metrics.

Experimental Results Typical results on public datasets (like ETTL or custom libraries): Single character recognition rates can reach above 95%, potentially dropping to 85% in complex scenarios (lighting/tilt conditions). Compared to traditional template matching methods (approximately 80% accuracy), BP neural networks demonstrate stronger robustness.

Improvement Directions CNN Integration: Replacing fully-connected BP networks with convolutional neural networks for enhanced feature extraction capabilities. Attention Mechanism Implementation: Improving recognition performance for blurred or occluded characters. Data Augmentation: Expanding sample diversity through rotation, noise injection, and other transformations.

The flexibility and trainability of BP neural networks make them a classical choice for license plate recognition, though with deep learning advancements, more complex hybrid models are emerging as new trends.