Handwritten Character Recognition Using CNN, KVM and SVM

One of the most active and difficult research areas in the field of image processing and pattern recognition has been handwritten character identification. It can be used for many tasks, such as creating organized text out of any written content, bank checks, and blind reading assistance. This research helps others with the understanding of handwritten characters for English alphabets without the need for feature extraction using only a multilayer feed-forward neural network. The character data sets each contains 26 alphabets. The neural network is trained using 50 different personality data sets. The trained network is used for classification as well as for recognition. Each character's size in the displayed system has been expanded to 30x20 pixels, followed by immediate training. To train the training variables for a character's scaled neural network. The findings show that the suggested approach matches feature extraction-based strategies in terms of handwritten character recognition rates.