pith. sign in

arxiv: 2011.08927 · v2 · pith:EKGV7ZYDnew · submitted 2020-11-16 · 💻 cs.CV · cs.CL

A New Dataset and Proposed Convolutional Neural Network Architecture for Classification of American Sign Language Digits

classification 💻 cs.CV cs.CL
keywords languagesigndatasetdigitspeopleamericanarchitectureconvolutional
0
0 comments X
read the original abstract

According to interviews with people who work with speech impaired persons, speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence. With this paper, to increase the quality of life of individuals with facilitating communication between individuals who use sign language and who do not know this language, a new American Sign Language (ASL) digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful created and published as Sign Language Digits Dataset on Kaggle Datasets web page, a proposal Convolutional Neural Network (CNN) architecture that can get 98% test accuracy on our dataset presented, and compared with the existing popular CNN models.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards

    cs.CL 2026-04 unverdicted novelty 5.0

    A survey indexes 120 sign-language datasets from 35 languages, identifies modality, annotation, and bias issues, and proposes a standardized 24-field datasheet with an open repository.