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ABSTRACT
This study harnesses the power of a Convolutional Neural Network (CNN) within a Deep Learning framework to achieve precise recognition of handwritten Hindi characters. Leveraging a dataset sourced from Kaggle, comprising 92,000 photos divided into both training and test sets, this model focuses on Devanagari characters handwritten by diverse individuals. The aim extends beyond digitizing characters; it serves as a versatile tool for training recognizers, writer identification, verification tests, and broader applications in text recognition.
The model, constructed on a TensorFlow backend using Keras libraries, exhibits a robust architecture. It integrates four CNN layers and three fully connected layers to process grayscale images of handwritten characters. These layers perform intricate operations: filters extract distinctive features to list at each convolutional layer, leveraging convolution, pooling, and flattening techniques to enhance feature extraction and pattern recognition.
Transitioning from CNN layers to fully connected layers, the model computes the probability score for each character, culminating in the identification of the character with the highest likelihood as the output. thus Impressively, this model achieves a remarkable 92.4% accuracy in character recognition, signifying its efficacy and reliability.
Despite the existence of comparable models, this innovative approach surpasses its predecessors, showcasing superior performance and heightened accuracy. so Its ability to outperform established models signifies its advancements in addressing the complexities of handwritten character recognition within the Devanagari script.
