Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset
This paper introduces a Convolutional Neural Network (CNN) model for Arabic Sign Language (AASL) recognition, using the AASL dataset. Recognizing the fundamental importance of communication for the hearing-impaired, especially within the Arabic-speaking deaf community, the study emphasizes the critical role of sign language recognition systems. The proposed methodology achieves outstanding accuracy, with the CNN model reaching 99.9% accuracy on the training set and a validation accuracy of 97.4%. This study not only establishes a high-accuracy AASL recognition model but also provides insights into effective dropout strategies. The achieved high accuracy rates position the proposed model as a significant advancement in the field, holding promise for improved communication accessibility for the Arabic-speaking deaf community.
PDF AbstractDatasets
Introduced in the Paper:
No Background RGB Arabic Alphabets Sign Language DatasetUsed in the Paper:
RGB Arabic Alphabet Sign Language (AASL) datasetResults from the Paper
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Image Classification | No Background RGB Arabic Alphabets Sign Language Dataset | ArabSignNet | Validation Accuracy | 97.4 | # 1 | |
Image Classification | RGB Arabic Alphabet Sign Language (AASL) dataset | ArabSignNet | Validation Accuracy | 97.4 | # 1 |