论文标题
基于卷积神经网络受损的人的基于手势的阿拉伯语手语识别
Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network
论文作者
论文摘要
阿拉伯语手语认可了使用深度学习方法来识别手势和手势的杰出研究成就。 “交流形式”一词是指听力受损的人进行交流的行动。这些行动对于普通人来说很难理解。由于阿拉伯语手语（ARSL）从一个领土到另一个国家，然后在州内的变化，对阿拉伯语手语（ARSL）的认可已成为一项艰巨的研究。卷积神经网络已封装在基于机器学习技术的拟议系统中。为了识别阿拉伯语手语，使用了可穿戴的传感器。这种方法已被使用了一种可以适合所有阿拉伯语手势的系统。这可以由当地阿拉伯社区的受损人民使用。该研究方法已以合理和中等的精度使用。最初开发了深层卷积网络，用于从传感设备收集的数据中提取特征。这些传感器可以可靠地识别阿拉伯语手语的30个手签名字母。使用带有可穿戴传感器的DG5-V手套捕获了数据集中的手动运动。出于分类目的，使用了CNN技术。建议的系统将阿拉伯语手语手势作为输入，并输出发声语音作为输出。结果被90％的人认可。
The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology. The term "forms of communication" refers to the actions used by hearing-impaired people to communicate. These actions are difficult for ordinary people to comprehend. The recognition of Arabic Sign Language (ArSL) has become a difficult study subject due to variations in Arabic Sign Language (ArSL) from one territory to another and then within states. The Convolution Neural Network has been encapsulated in the proposed system which is based on the machine learning technique. For the recognition of the Arabic Sign Language, the wearable sensor is utilized. This approach has been used a different system that could suit all Arabic gestures. This could be used by the impaired people of the local Arabic community. The research method has been used with reasonable and moderate accuracy. A deep Convolutional network is initially developed for feature extraction from the data gathered by the sensing devices. These sensors can reliably recognize the Arabic sign language's 30 hand sign letters. The hand movements in the dataset were captured using DG5-V hand gloves with wearable sensors. For categorization purposes, the CNN technique is used. The suggested system takes Arabic sign language hand gestures as input and outputs vocalized speech as output. The results were recognized by 90% of the people.
