## 2 Related Works
's proposed methodology employs a two-layer image processing technique. In the first layer, images are processed as a whole for training purposes, while the second layer focuses on extracting hand landmarks to refine the recognition process. This approach is implemented through a multiheaded convolutional neural network (CNN) model, which effectively manages the dual processing tasks [20]. [PERSON] et al. proposed a novel Indian Sign Language (ISL) recognition system that combines Speeded Up Robust Features (SURF), support vector machine (SVM), and convolutional neural network (CNN) to improve communication for the hearing and speech-impaired communities. Through extensive experiments with a well-defined ISL gesture dataset, the system achieved over 90% accuracy, with CNN playing a key role in enhancing feature extraction and classification [21]. [PERSON] et al. proposed a model that utilizes long short-term memory (LSTM) and gated recurrent unit (GRU) networks to detect and recognize gestures from isolated video frames of Indian Sign Language (ISL). The authors developed their own dataset, IISL2020, and achieved approximately 97% accuracy in recognizing 11 different signs using a combination of LSTM and GRU layers [22]. More recently, transformer models have been applied to sign language recognition, showing promising results. Transformers, known for their capability to handle long-range dependencies and parallelize training, are particularly effective for sequential data, such as sign language videos. [PERSON] et al. proposed a novel approach to recognizing static Indian Sign Language using a transformer encoder. The proposed system, which utilizes a vision transformer to process sign language through positional embedding patches and a transformer block with self-attention layers, significantly outperforms traditional convolutional architectures. Achieving an accuracy of 99.29% with minimal training, the proposed SIGNFORMER demonstrates robustness and effectiveness in real-world applications [23]. [PERSON] et al. addressed the gap in existing research by applying pretrained models and vision transformers, typically used in image classification, to Arabic Sign Language (ArSL). Utilizing a dataset of 54,049 images representing 32 different Arabic letters, the research compared transfer learning models, including MobileNet, Xception, and ResNet, with CNN architectures. The results show that transfer learning approaches, particularly ResNet and InceptionResNet, achieved high accuracy of 98%, demonstrating the potential of these methods for enhancing sign language recognition in low-resourced languages [24]. Recent research in sign language recognition leverages keypoint-based approaches, drawing on methods proposed in [25]. This study significantly improves 3D human pose estimation by achieving an 18% reduction in prediction error compared to previous unsupervised methods, demonstrating effective 3D pose prediction from 2D inputs without relying on 3D training data. The results, validated on the Human3.6M dataset, show that PoseNet3D outperforms existing techniques, delivering robust and accurate 3D pose estimations with natural and realistic transitions across frames. The success of the model is attributed to the joint fine-tuning of teacher and student networks using temporal, self-consistency, and adversarial losses, enhancing overall prediction accuracy. [PERSON] et al. [26] explored the use of neuroevolution techniques to optimize deep neural networks for recognizing American Sign Language (ASL) fingerspelling. By employing hyperheuristic algorithms to fine-tune network architectures and hyperparameters, the study achieved a high mean accuracy of 97.44% on a dataset of 1678 images. The research highlights the potential of these techniques in enhancing the accuracy and efficiency of sign language recognition systems, making them more effective for individuals with hearing impairments.