Vietnamese Sign Language Recognition for People with Hearing Impairment using Digital Library

Tran Thi Thuy Ha; Phan Thi Ha

doi:10.35940/ijitee.I1287.15080726

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Published: 30-07-2026

DOI: https://doi.org/10.35940/ijitee.I1287.15080726

Keywords:

Random Forest, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM, Hearing Impairment, Identification, Sign Language, Library, Phrase

Tran Thi Thuy Ha

Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT) in Vietnam, Ha Dong, (Ha Noi), Vietnam.

https://orcid.org/0009-0005-6734-1509

Phan Thi Ha

Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT) in Vietnam, Ha Dong, (Ha Noi), Vietnam.

https://orcid.org/0009-0003-2521-0717

Abstract

This paper proposes a method for building a specialised sign language dataset for deaf-mute sign language recognition in digital libraries, comprising 120 signs and 65 phrases, collected from 25 people with hearing impairments in Hanoi and Ho Chi Minh City. The data were collected under various conditions and yielded approximately 125,000 video samples after processing. Based on experimental results with Random Forest [3], pure LSTM [1], and CNN-LSTM models [4], the CNN-LSTM model yields the best results and is selected for the development of Vietnamese sign language recognition services, demonstrating high performance, stability, and suitability for practical implementation requirements.

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Issue

Vol. 15 No. 8 (2026): Volume-15 Issue-8, July 2026

Section

Articles

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CC-BY-NC-ND 4.0

How to Cite

[1]

Tran Thi Thuy Ha and Phan Thi Ha , Trans., “Vietnamese Sign Language Recognition for People with Hearing Impairment using Digital Library”, IJITEE, vol. 15, no. 8, pp. 7–11, Jul. 2026, doi: 10.35940/ijitee.I1287.15080726.

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