Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Possibility of Using Waste Materials as Substitutes for Gravel or Water in Concrete Mix.

Materials (Basel, Switzerland)·2023

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 16, 2025

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

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

Published on: December 15, 2023

Application for Recognizing Sign Language Gestures Based on an Artificial Neural Network.

Kamil Kozyra¹, Karolina Trzyniec², Ernest Popardowski¹

¹Ailleron SA, Jana Pawła II 43b, 31-864 Krakow, Poland.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

This study developed a deep learning application for recognizing American Sign Language (ASL) signs using convolutional neural networks. The system achieved 99% effectiveness on its training set, paving the way for improved accessibility.

Keywords:

convolutional neural networks deep learning algorithm machine image recognition systems

More Related Videos

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Published on: January 26, 2024

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

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

Published on: March 28, 2025

Related Experiment Videos

Last Updated: Aug 16, 2025

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

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

Published on: December 15, 2023

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Published on: January 26, 2024

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

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

Published on: March 28, 2025

Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

American Sign Language (ASL) recognition is crucial for communication accessibility.
Deep learning, particularly convolutional neural networks (CNNs), offers powerful tools for image recognition tasks.

Purpose of the Study:

To develop and implement a deep learning application for recognizing ASL signs.
To evaluate the effectiveness of CNN architectures for ASL sign recognition.

Main Methods:

Development of a comprehensive training dataset for ASL signs.
Implementation of a data preprocessing module to convert images for neural network input.
Selection and development of an appropriate CNN model architecture.
Training and verification of the neural network model.

Main Results:

An internet application capable of recognizing ASL signs from user-submitted photos was successfully implemented.
The developed neural network model achieved a 99% effectiveness ratio on the training dataset.
Analysis of the application's performance was conducted.

Conclusions:

The deep learning application demonstrates high effectiveness in recognizing ASL signs.
Recommendations for further improvements to the application's operational performance are provided.