Ghanaian Sign Language Recognition Using Deep Learning

Odartey, Lamptey Khalid; Huang, Yonfeng; Asantewaa, Effah E.; Agbedanu, Promise Ricardo

doi:10.1145/3357777.3357784

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Using transfer learning to train the last layer of the model on a target dataset of 10 classes, they reported highly accurate results for their model. Some authors 16 proposed a CNN model and fine-tuned pre-trained Visual Geometry Group (VGG) models. They conducted different experiments on a dataset with 33 classes of static hand gestures to evaluate these models.…”

Section: Introductionmentioning

confidence: 99%

Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

Fadhil

Mahdi²,

Abbas³

2023

Baghdad Sci.J

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A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different methods. First, the models were initialized with random weights and trained from scratch. Afterward, the pre-trained models were examined as feature extractors. Finally, the pre-trained models were fine-tuned with intermediate layers. Fine-tuning was conducted on three levels: the fifth, fourth, and third blocks, respectively. The models were evaluated through recognition experiments using hand gesture images in the Arabic sign language acquired under different conditions. This study also provides a new hand gesture image dataset used in these experiments, plus two other datasets. The experimental results indicated that the proposed models can be used with intermediate layers to recognize hand gesture images. Furthermore, the analysis of the results showed that fine-tuning the fifth and fourth blocks of these two models achieved the best accuracy results. In particular, the testing accuracies on the three datasets were 96.51%, 72.65%, and 55.62% when fine-tuning the fourth block and 96.50%, 67.03%, and 61.09% when fine-tuning the fifth block for the first model. The testing accuracy for the second model showed approximately similar results.

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Section: Introductionmentioning

confidence: 99%

Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

Fadhil

Mahdi²,

Abbas³

2023

Baghdad Sci.J

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show abstract

“…sub-Saharan Africa, there is a dearth of sign language datasets. Aside from an instance of a slightly-complex South African Sign Language dataset which uses kinetic gloves [9] and a very small corpus of Ghanaian Sign Language which serves as a proof of concept [10], no further work has been done in creating standard datasets for sub-Saharan African sign languages.…”

Section: Introductionmentioning

confidence: 99%

Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language

Kolawole,

Osakuade,

Saxena

et al. 2021

Preprint

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Through this paper we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech.5th Workshop on Machine Learning for the Developing World at NeurIPS 2021, held virtually.

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Prototype App Mobile for Real Time American Sign Language Recognition Based on Deep Learning

Ramirez,

L. Ramírez,

Tito

et al. 2024

Lecture Notes in Networks and Systems

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Ghanaian Sign Language Recognition Using Deep Learning

Cited by 5 publications

References 7 publications

Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language

Prototype App Mobile for Real Time American Sign Language Recognition Based on Deep Learning

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