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All Journal The Indonesian Journal of Computer Science
Moape, Tebatso
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Evaluating the Impact of Deep Learning Model Architecture on Sign Language Recognition Accuracy in Low-Resource Context Moape, Tebatso; Muzambi, Absolom; Chimbo, Bester
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4493

Abstract

Deep learning models are well-known for their reliance on large training datasets to achieve optimal performance for specific tasks. These models have revolutionized the field of machine learning, including achieving high accuracy rates in image classification tasks. As a result, these models have been used for sign language recognition. However, the models often underperform in low-resource contexts. Given the country-specific nature of sign languages, this study examines the effectiveness and performance of Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), hybrid model (CNN + Recurrent Neural Networks (RNN)), and VGG16 deep learning architectures in recognizing South African Sign Language (SASL) under a data-constrained context. The models were trained and evaluated using a dataset of 12420 training images representing 26 static SASL alphabets, and 4050 validation images. The paper's primary objective is to determine the optimal methods and settings for improving sign recognition models in low-resource contexts. The performance of the models was evaluated across multiple image dimensions trained for 60 epochs to analyze each model's adaptability and efficiency under varying computational parameters. The experiments showed that the ANN and CNN models consistently achieved high accuracy with lower computational requirements, making them well-suited for low-resource contexts.
A Culture-Aware Bidirectional IsiXhosa-English Neural Machine Translation Model Using MarianMT Moape, Tebatso; Mohale, Thuto Siyamthanda; Bester, Chimbo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4714

Abstract

Machine translation for low-resource African languages faces significant challenges due to limited data availability and complex linguistic features such as rich morphology, agglutinative grammar, and rich cultural expressions. This study proposes and develops a culturally aware machine translation model for isiXhosa-English language pairs using the MarianMT transformer-based model. We combine traditional parallel corpora with culturally enriched datasets, addressing the unique challenges of isiXhosa's linguistic intricacies. The proposed model was trained on a carefully curated dataset of 127,690 parallel sentences and used SentencePiece tokenization for handling agglutinative morphology. Our approach achieved a BLEU score of 58.79, marking a substantial improvement over previous methods, typically scoring between 20.9 and 37.11. The results demonstrate that integrating cultural context and linguistic specificities into the translation model substantially improves translation quality for low-resource languages. The study's findings suggest that considering cultural context, combined with appropriate model architecture and data preprocessing strategies, can lead to more accurate and culturally aware machine translation systems.
Co-Authors Bester, Chimbo Chimbo, Bester Mohale, Thuto Siyamthanda Muzambi, Absolom