Character Recognition Using BP Neural Networks

Character recognition using Backpropagation Neural Networks. The BP neural network algorithm is a learning method that converts sample input-output problems into nonlinear optimization problems and solves weight values through iterative gradient algorithm operations. To achieve effective single-character recognition, we employ BP networks for classification supplemented with linear perceptrons. This algorithm offers simplicity in implementation, high recognition rates, and adaptability to various high-noise environments for printed character recognition. From a coding perspective, the implementation typically involves forward propagation for output calculation, error computation using target outputs, and backward propagation for weight adjustments through partial derivative calculations. The network architecture often includes input layers matching character pixel dimensions, hidden layers with sigmoid/tanh activation functions, and output layers corresponding to character classes. Through this method, we enhance character recognition accuracy, enabling the technology to function effectively across broader application scenarios. Additional code considerations include learning rate optimization, momentum term integration for convergence improvement, and batch processing techniques for handling character datasets.