School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566, South Korea.
Division of Computer Engineering, Hankuk University of Foreign Studies, Seoul, South Korea.
Sci Rep. 2022 Jul 13;12(1):11964. doi: 10.1038/s41598-022-15998-7.
Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.
手语识别受到一些问题的挑战，例如对手势的准确跟踪、手部遮挡和计算成本高。最近，它受益于深度学习技术的进步。然而，这些更大更复杂的方法无法处理长期的序列数据，并且在捕获有用信息方面的信息处理和学习效率较差。为了克服这些挑战，我们提出了一种用于印度手语识别的集成 MediaPipe 优化门控循环单元（MOPGRU）模型。具体来说，我们通过将其乘以重置门来改进标准 GRU 单元的更新门，以在一次筛选中丢弃过去的冗余信息。通过从重置门的结果中获得反馈，对当前输入给予额外的关注。此外，我们在 GRU 单元的输出层中用指数线性单元激活替换标准 GRU 中的双曲正切激活，用 Softsign 激活替换 SoftMax。因此，与其他序列模型相比，我们提出的 MOPGRU 模型实现了更好的预测精度、高效率的学习、信息处理能力和更快的收敛速度。
