Enhancing Arabic Sign Language Interpretation: Leveraging Convolutional Neural Networks and Transfer Learning

Saad Al Ahmadi, Farah Muhammad () and Haya Al Dawsari
Additional contact information
Saad Al Ahmadi: Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Farah Muhammad: Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Haya Al Dawsari: Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Mathematics, 2024, vol. 12, issue 6, 1-22

Abstract: In a world essentializing communication for human connection, the deaf community encounters distinct barriers. Sign language, their main communication method is rich in hand gestures but not widely understood outside their community, necessitating interpreters. The existing solutions for sign language recognition depend on extensive datasets for model training, risking overfitting with complex models. The scarcity of details on dataset sizes and model specifics in studies complicates the scalability and verification of these technologies. Furthermore, the omission of precise accuracy metrics in some research leaves the effectiveness of gesture recognition by these models in question. The key phases of this study are Data collection, Data preprocessing, Feature extraction using CNN and finally transfer learning-based classification. The purpose of utilizing CNN and transfer learning is to tap into pre-trained neural networks for optimizing performance on new, related tasks by reusing learned patterns, thus accelerating development and improving accuracy. Data preprocessing further involves resizing of images, normalization, standardization, color space conversion, augmentation and noise reduction. This phase is capable enough to prune the image dataset by improving the efficiency of the classifier. In the subsequent phase, feature extraction has been performed that includes the convolution layer, feature mapping, pooling layer and dropout layer to obtain refined features from the images. These refined features are used for classification using ResNet. Three different datasets are utilized for the assessment of proposed model. The ASL-DS-I Dataset includes a total of 5832 images of hand gestures whereas, ASL-DS-II contains 54,049 images and ASL-DS-III dataset includes 7857 images adopted from specified web links. The obtained results have been evaluated by using standard metrics including ROC curve, Precision, Recall and F-measure. Meticulous experimental analysis and comparison with three standard baseline methods demonstrated that the proposed model gives an impressive recognition accuracy of 96.25%, 95.85% and 97.02% on ASL-DS-I, ASL-DS-II and ASL-DS-III, respectively.

Keywords: Arabic Sign Language; CNN; transfer learning; ResNet-152 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/6/823/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/6/823/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:6:p:823-:d:1355140

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().