Handwritten Character and Digit Recognition

EasyChair Preprint 10209

Abstract

This research paper focuses on the development and implementation of a machine learning system for handwritten character and digit recognition. With the increasing use of digital devices, there is a growing need for accurate recognition of handwritten characters and digits. In this paper, we present a comprehensive study of various techniques and algorithms used in the field of handwritten character and digit recognition. We discuss the challenges associated with this task and present a detailed analysis of the performance of different machine learning models in recognizing handwritten characters and digits. Our experimental results show that deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) outperform traditional machine learning models. Additionally, we explore the use of data augmentation techniques to improve the accuracy of the models. The results of our study demonstrate the potential of machine learning in the field of handwritten character and digit recognition, and its potential applications in various domains, including document analysis, handwriting recognition, and digitization of historical documents.

Keyphrases: Machine Learning Model, character recognition, image classification

@booklet{EasyChair:10209,
  author    = {Sanya Tyagi and Simran Sagar and Shreya Agarwal},
  title     = {Handwritten Character and Digit Recognition},
  howpublished = {EasyChair Preprint 10209},
  year      = {EasyChair, 2023}}