Pavan Kumar Vangala
Independent Researcher
India
Abstract
This manuscript examines the implementation of neural networks in handwriting recognition, focusing on technologies and methodologies available up to 2015. The study reviews the evolution from early multilayer perceptrons to convolutional and recurrent neural architectures, evaluates performance metrics across benchmark datasets, and presents a statistical analysis comparing recognition accuracy and training efficiency. A methodology for designing, training, and validating neural models on handwritten character datasets is detailed. Results demonstrate significant accuracy improvements with convolutional architectures, while highlighting trade-offs in computational complexity. Research gaps are identified in generalization to diverse scripts and in real-time deployment constraints. The findings aim to guide engineering practitioners in selecting suitable neural network approaches for handwriting recognition systems within the technological landscape of 2015.
Keywords neural networks, handwriting recognition, multilayer perceptron, convolutional neural network, statistical analysis
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