Characterization and recognition of handwritten digits using Julia

TL;DR

This paper presents a hybrid neural network model implemented in Julia for recognizing handwritten digits in the MNIST dataset, demonstrating effective feature extraction and recognition capabilities.

Contribution

The study introduces a novel hybrid neural network approach that effectively recognizes handwritten digits and analyzes its layer-wise feature extraction and auto-encoding systems.

Findings

Achieved high accuracy on MNIST dataset

Demonstrated layer-wise feature extraction capabilities

Analyzed auto-encoding and variational auto-encoding systems

Abstract

Automatic image and digit recognition is a computationally challenging task for image processing and pattern recognition, requiring an adequate appreciation of the syntactic and semantic importance of the image for the identification ofthe handwritten digits. Image and Pattern Recognition has been identified as one of the driving forces in the research areas because of its shifting of different types of applications, such as safety frameworks, clinical frameworks, diversion, and so on.In this study, for recognition, we implemented a hybrid neural network model that is capable of recognizing the digit of MNISTdataset and achieved a remarkable result. The proposed neural model network can extract features from the image and recognize the features in the layer by layer. To expand, it is so important for the neural network to recognize how the proposed modelcan work in each layer, how it can generate output, and so on. Besides, it also can recognize the auto-encoding system and the variational auto-encoding system of the MNIST dataset. This study will explore those issues that are discussed above, and the explanation for them, and how this phenomenon can be overcome.