Decoding Handwritten Characters using Convolutional Neural Networks (CNNs)

Handwritten Character Recognition (HCR) plays a crucial role in various applications, from document analysis to form processing. HCR technology allows computers to interpret handwritten characters from diverse sources like physical documents, photographs, and digital scans. This research explores the use of Convolutional Neural Networks (CNNs), a type of Artificial Neural Network (ANN), to design, train, and deploy a model for handwritten character recognition and classification. The CNN model was developed using the EMNIST dataset and implemented in Python. Experimental results demonstrate that the CNN achieves an accuracy of 98% on a standard dataset. The potential for further improvement exists by increasing training epochs and expanding the training datasets. These refinements could lead to accuracy exceeding 99% and open doors for applications such as handwritten interfaces, Optical Character Recognition (OCR), and advanced document analysis.