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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

Experimental evaluation of a BiLSTM network for Indian sign language recognition

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pp. 1633–1641Vol. 47Issue 5-AMay 2026DOI: 10.47974/JIOS-2253XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2253
Pages:
1633–1641

Abstract

For the Hearing-and speech-impaired (HSI) individuals in India, the ISL functions as a standard and helps communication. This paper proposed an efficient Indian Sign Language recognition system that focused on computational efficiency and accuracy, which is achieved by intelligent temporal modelling, key frame selection and lower-dimension feature extraction. The frames are used to extract features with the Mediapipe Holistic model (MPHm) to form a low-dimensional feature vector. A BiLSTM network, which learns temporal dependencies in both forward and backward directions, is used to model the sequential characteristics of signs. The model has been trained and evaluated on 22 frequently-used ISL isolated words from a public-domain pre-recorded video dataset called INCLUDE and achieved an accuracy of 96.91% on these sample strata. In addition to that, the performance of the model is also evaluated on the INCLUDE 50 dataset, and an accuracy of 98.37% has been achieved.

Keywords

ASLRSign languageMPHmHearing-and speech-impairedIndian sign language

Subject Classifications

68T07

References

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