A Deep Learning based Approach for Recognition of Arabic Sign Language Letters

No one can deny that the deaf-mute community has communication problems in daily life. Advances in artificial intelligence over the past few years have broken through this communication barrier. The principal objective of this work is creating an Arabic Sign Language Recognition system (ArSLR) for recognizing Arabic letters. The ArSLR system is developed using our image pre-processing method to extract the exact position of the hand and we proposed architecture of the Deep Convolutional Neural Network (CNN) using depth data. The goal is to make it easier for people who have hearing problems to interact with normal people. Based on user input, our method will detect and recognize hand-sign letters of the Arabic alphabet automatically. The suggested model is anticipated to deliver encouraging results in the recognition of Arabic sign language with an accuracy score of 97,07%. We conducted a comparison study in order to evaluate proposed system, the obtained results demonstrated that this method is able to recognize static signs with greater accuracy than the accuracy obtained by similar other studies on the same dataset used.

@article{Hdioud2023,
  title     = {A Deep Learning based Approach for Recognition of Arabic Sign Language Letters},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {4},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Boutaina Hdioud and Mohammed El Haj Tirari},
  doi       = {10.14569/IJACSA.2023.0140447},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140447}
}