program for character recognition using back propagation network in matlab

To program character recognition using backpropagation network in MATLAB, you can follow these steps:

1. Preprocess the character images: Convert the character images into a suitable format for the neural network. You may need to resize the images, convert them to grayscale, and normalize the pixel values.

2. Prepare the training data: Divide your dataset into a training set and a testing set. The training set will be used to train the neural network, while the testing set will be used to evaluate its performance.

3. Create the neural network: In MATLAB, you can use the Neural Network Toolbox to create a backpropagation network. Define the number of input neurons, hidden neurons, and output neurons based on the size of your character images and the number of classes you want to recognize.

   main.m
   net = feedforwardnet([hidden_neurons]);
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4. Train the neural network: Train the backpropagation network using the training set. Set the training parameters such as the learning rate, maximum number of epochs, and the error tolerance.

   main.m
   net.trainParam.lr = learning_rate;
   net.trainParam.epochs = max_epochs;
   net.trainParam.goal = error_tolerance;
   
   net = train(net, training_data, training_labels);
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5. Test the trained network: Evaluate the performance of the trained neural network using the testing set. Pass the testing set through the trained network and compare the outputs with the expected labels.

   main.m
   outputs = net(testing_data);
   predicted_labels = vec2ind(outputs);
   accuracy = sum(predicted_labels == testing_labels) / numel(testing_labels) * 100;
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6. Improve the network performance: If the network's accuracy is not satisfactory, you can try different training parameters, adjust the number of hidden neurons, or explore other techniques such as data augmentation or feature extraction to improve the performance.

Note: This is a high-level overview of programming character recognition using a backpropagation network in MATLAB. Implementing a fully functional character recognition system may involve additional steps and techniques.