American Sign Language Fingerspelling Recognition Using A Pre-Trained Googlenet Convolutional Neural Network

Nandini Santosh Sarode

Title:
American Sign Language Fingerspelling Recognition Using A Pre-Trained Googlenet Convolutional Neural Network

Authors:
Nandini Santosh Sarode

Cite This Article :

Nandini Santosh Sarode "American Sign Language Fingerspelling Recognition Using A Pre-Trained Googlenet Convolutional Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.262-271, URL: https://www.ijtsrd.com/papers/ijtsrd101622.pdf

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Abstract :
A practical sign language translator is an essential way for communication between the deaf community and the general public. So here we present the development and implementation of an American Sign Language (ASL) fingerspelling translator based on a convolutional neural network. We utilize a pre-trained GoogLeNet architecture trained . We produced a robust model which classifies letters a-z correctly with first-time users and another that correctly distinguish letters a-k in a majority of cases. The limitations of the dataset and the encouraging results achieved, we are confident that with further research and more data, we can produce a generalized translator for all ASL letters.

Keywords :
ASL, Sign Language Character Recognition, Convolution Neural Network, Computer Vision, Machine Learning

Publication Details:

Unique Identification Number : IJTSRD101622

Published In : Special Issue | Smart Innovations in Computer Science and Applications, March 2026

Page Number(s) : 262-271

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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