Enhanced handwritten digit recognition using optimally selected optimizer for an ANN.

Handwritten digit recognition is a complex problem that has stumped even the brilliant minds of this century. Getting precise results from different handwritten samples has been a challenge that needs to be addressed due to the occurrence of this issue in several sectors like document verification, post mail, deciphering, etc. Hence, we introduce our paper as a response to the requirement of an accurate model that can acutely recognize and then predict the handwriting of a variety of individuals with ease. Our model aims to do number recognition through the implementation of neural networks. We tested out different models with each optimizer to verify which model provided the best performance and with which optimizer. Optimizers are an inherent part of Deep learning, and they are used to upgrade the weights, so the model can learn accordingly and get a more accurate system. Instead of just comparing the performance of various optimizers with only one model, we compared different model performances, while trying to select the optimizer that would best suit that learning model. Rigorous training and experimentalizing have resulted in an accuracy of 98.55% for an ANN model employed with an Adagrad optimizer. [ABSTRACT FROM AUTHOR]