NeuroWrite: Predictive Handwritten Digit Classification using Deep
  Neural Networks
Kottakota Asish P. Sarath Teja R. Kishan Chander Dr. D. Deva Hema
The rapid evolution of deep neural networks has revolutionized the field of
machine learning, enabling remarkable advancements in various domains. In this
article, we introduce NeuroWrite, a unique method for predicting the
categorization of handwritten digits using deep neural networks. Our model
exhibits outstanding accuracy in identifying and categorising handwritten
digits by utilising the strength of convolutional neural networks (CNNs) and
recurrent neural networks (RNNs).In this article, we give a thorough
examination of the data preparation methods, network design, and training
methods used in NeuroWrite. By implementing state-of-the-art techniques, we
showcase how NeuroWrite can achieve high classification accuracy and robust
generalization on handwritten digit datasets, such as MNIST. Furthermore, we
explore the model's potential for real-world applications, including digit
recognition in digitized documents, signature verification, and automated
postal code recognition. NeuroWrite is a useful tool for computer vision and
pattern recognition because of its performance and adaptability.The
architecture, training procedure, and evaluation metrics of NeuroWrite are
covered in detail in this study, illustrating how it can improve a number of
applications that call for handwritten digit classification. The outcomes show
that NeuroWrite is a promising method for raising the bar for deep neural
network-based handwritten digit recognition.