Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Thermal enhancement of ternary hybrid Casson nanofluid in porous media: a sensitivity analysis study.

Scientific reports·2026
Same author

Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning.

Computational intelligence and neuroscience·2022
Same author

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification.

Computational intelligence and neuroscience·2022
Same author

Personality Prediction with Hybrid Genetic Programming using Portable EEG Device.

Computational intelligence and neuroscience·2022
Same author

Yoga Pose Estimation and Feedback Generation Using Deep Learning.

Computational intelligence and neuroscience·2022
Same author

Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

Journal of healthcare engineering·2022
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: The Design of Adolescents' Physical Health Prediction System Based on Deep Reinforcement Learning.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: The Navigation of Mobile Robot in the Indoor Dynamic Unknown Environment Based on Decision Tree Algorithm.

Computational intelligence and neuroscience·2025
See all related articles
  1. Home
  3. A Machine Learning And Deep Learning Approach For Recognizing Handwritten Digits.
  1. Home
  3. A Machine Learning And Deep Learning Approach For Recognizing Handwritten Digits.

Related Experiment Video

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

725

A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits.

Ayushi Sharma1, Harshit Bhardwaj1, Arpit Bhardwaj2

  • 1Department of Computer Science and Engineering SoE, Galgotias University, Greater Noida, India.

Computational Intelligence and Neuroscience
|July 25, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This study compares Machine Learning and Deep Learning algorithms for handwritten digit recognition using optical character recognition (OCR). Convolutional Neural Networks (CNNs) achieved the highest accuracy at 98.83%.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K

Related Experiment Videos

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

725
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Optical Character Recognition (OCR) is a key technology for digitizing documents.
  • Handwritten digit recognition is a challenging subfield within OCR.
  • The Modified National Institute of Standards and Technology (NIST) database provides valuable datasets for this task.

Purpose of the Study:

  • To evaluate and compare the performance of Machine Learning and Deep Learning algorithms for handwritten digit recognition.
  • To determine the classification accuracy of different algorithms on unique handwritten digits.
  • To identify the most effective algorithm for this specific OCR task.

Main Methods:

  • Utilized unique handwritten digits from the NIST database.
  • Implemented and trained Machine Learning algorithms.
  • Implemented and trained Deep Learning algorithms, specifically Convolutional Neural Networks (CNNs).
  • Compared the classification accuracy of the implemented algorithms.

    • Main Results:

    • The Convolutional Neural Network (CNN) classifier demonstrated superior performance.
    • CNN achieved the highest classification accuracy of 98.83% in recognizing handwritten digits.
    • Comparative analysis highlighted the effectiveness of deep learning approaches over traditional machine learning for this task.

    Conclusions:

    • Deep learning, particularly CNNs, offers state-of-the-art performance for handwritten digit recognition.
    • The study validates the efficacy of CNNs for optical character recognition tasks.
    • Accurate handwritten digit recognition is crucial for various applications, including document analysis and data entry.