T and L Letter Recognition using BP Neural Network with Improved Momentum Method

The T and L letter recognition system using BP neural network with improved momentum method is a specialized neural network model designed for distinguishing between the letters T and L. This approach enhances training effectiveness by incorporating a modified momentum term into the backpropagation algorithm. The momentum term implementation helps accelerate convergence speed and improves recognition accuracy by reducing oscillations during gradient descent. In code implementation, this typically involves adding a velocity term to the weight update formula: Δw(t) = η∇E + αΔw(t-1), where η represents the learning rate, ∇E denotes the gradient of the error function, and α is the momentum coefficient. This modified algorithm allows for more stable weight adjustments and better escape from local minima. Through this enhanced methodology, the system achieves superior performance in T and L character recognition, contributing significantly to research in optical character recognition and pattern recognition fields. The implementation typically involves preprocessing input patterns, designing appropriate network architecture with hidden layers, and tuning hyperparameters like learning rate and momentum coefficient for optimal performance.