Recognizing Handwritten Digits and Alphabets Using Machine Learning on Python

EasyChair Preprint 3162

Abstract

Handwritten character recognition has recently been a subject of interest among the researchers thanks to the evolution of assorted Machine Learning, Deep Learning and computer Vision algorithms.

Handwritten digit recognition is the ability of a computing system to acknowledge the written inputs like dogs, characters etc from a large sort of sources like emails, papers, images, letters etc. This has been a subject of analysis for many years. A number of the analysis areas embrace signature verification, bank check processing, postal address interpretation from envelopes etc.

In this project we tend to try and demonstrate how by using modern libraries like Scikit-Learn and NumPy and robust programming languages like Python, we will produce our very own neural networks to acknowledge handwritten digits.

Keyphrases: Computing system, Handwritten Digit Recognition, computer Vision, deep learning, machine learning, neural networks

@booklet{EasyChair:3162,
  author    = {Pratik Sharma},
  title     = {Recognizing Handwritten Digits and Alphabets Using Machine Learning on Python},
  howpublished = {EasyChair Preprint 3162},
  year      = {EasyChair, 2020}}