Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Nur-Sultan 010008, Kazakhstan.
Institute of Economics, Information Technologies and Professional Education, Zangir Khan West Kazakhstan Agrarion-Technical University, Uralsk 090000, Kazakhstan.
Sensors (Basel). 2022 Sep 1;22(17):6621. doi: 10.3390/s22176621.
Technologies for pattern recognition are used in various fields. One of the most relevant and important directions is the use of pattern recognition technology, such as gesture recognition, in socially significant tasks, to develop automatic sign language interpretation systems in real time. More than 5% of the world's population-about 430 million people, including 34 million children-are deaf-mute and not always able to use the services of a living sign language interpreter. Almost 80% of people with a disabling hearing loss live in low- and middle-income countries. The development of low-cost systems of automatic sign language interpretation, without the use of expensive sensors and unique cameras, would improve the lives of people with disabilities, contributing to their unhindered integration into society. To this end, in order to find an optimal solution to the problem, this article analyzes suitable methods of gesture recognition in the context of their use in automatic gesture recognition systems, to further determine the most optimal methods. From the analysis, an algorithm based on the palm definition model and linear models for recognizing the shapes of numbers and letters of the Kazakh sign language are proposed. The advantage of the proposed algorithm is that it fully recognizes 41 letters of the 42 in the Kazakh sign alphabet. Until this time, only Russian letters in the Kazakh alphabet have been recognized. In addition, a unified function has been integrated into our system to configure the frame depth map mode, which has improved recognition performance and can be used to create a multimodal database of video data of gesture words for the gesture recognition system.
模式识别技术在各个领域都有应用。其中一个最相关和重要的方向是使用模式识别技术，例如手势识别，来完成具有社会意义的任务，实时开发自动手语翻译系统。全世界有超过 5%的人口，约 4.3 亿人，包括 3400 万失聪人士，他们并不总能使用手语翻译员的服务。近 80%的听力障碍者生活在中低收入国家。开发低成本的自动手语翻译系统，不使用昂贵的传感器和独特的摄像机，将改善残疾人士的生活，促进他们无障碍地融入社会。为此，为了找到问题的最佳解决方案，本文分析了在自动手势识别系统中使用手势识别的合适方法，以进一步确定最优化的方法。在此基础上，提出了一种基于手掌定义模型和线性模型的识别哈萨克手语数字和字母形状的算法。所提出算法的优点在于，它可以完全识别哈萨克字母表中的 41 个字母，而在此之前，哈萨克字母表中的字母只识别俄语字母。此外，我们的系统还集成了一个统一的功能，可以配置帧深度图模式，这提高了识别性能，可用于创建手势识别系统的手势单词视频数据的多模态数据库。
