To train character images in MATLAB, start by preprocessing the images. Convert them to grayscale or binary format and resize them to a standard size. Extract features using methods like HOG, LBP, or custom descriptors.
Use MATLAB’s fitcecoc function to train a multi-class classifier, such as SVM, on the feature vectors. Alternatively, employ the Deep Learning Toolbox to train a CNN for end-to-end learning.
Evaluate the model’s performance using metrics like accuracy or confusion matrices. Fine-tune hyperparameters and preprocess steps as needed. Save the trained model for inference or deployment in recognition tasks.