Salau Ayodeji Olalekan, Tamiru Nigus Kefyalew, Abeje Bekalu Tadele
Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria.
Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Heliyon. 2024 Sep 21;10(19):e38265. doi: 10.1016/j.heliyon.2024.e38265. eCollection 2024 Oct 15.
Hearing-impaired people use sign language as a means of communication with those with no hearing disability. It is therefore difficult to communicate with hearing impaired people without the expertise of a signer or knowledge of sign language. As a result, technologies that understands sign language are required to bridge the communication gap between those that have hearing impairments and those that dont. Ethiopian Amharic alphabets sign language (EAMASL) is different from other countries sign languages because Amharic Language is spoken in Ethiopia and has a number of complex alphabets. Presently in Ethiopia, just a few studies on AMASL have been conducted. Previous works, on the other hand, only worked on basic and a few derived Amharic alphabet signs. To solve this challenge, in this paper, we propose Machine Learning techniques such as Support Vector Machine (SVM) with Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG), and their hybrid features to recognize the remaining derived Amharic alphabet signs. Because CNN is good for rotation and translation of signs, and HOG works well for low quality data under strong illumination variation and a small quantity of training data, the two have been combined for feature extraction. CNN (Softmax) was utilized as a classifier for normalized hybrid features in addition to SVM. SVM model using CNN, HOG, normalized, and non-normalized hybrid feature vectors achieved an accuracy of 89.02%, 95.42%, 97.40%, and 93.61% using 10-fold cross validation, respectively. With the normalized hybrid features, the other classifier, CNN (sofmax), produced a 93.55% accuracy.
听力受损者使用手语作为与听力正常者交流的方式。因此，如果没有手语翻译的专业技能或手语知识，就很难与听力受损者进行交流。因此，需要能够理解手语的技术来弥合听力受损者和非听力受损者之间的沟通差距。埃塞俄比亚阿姆哈拉语字母手语（EAMASL）与其他国家的手语不同，因为阿姆哈拉语在埃塞俄比亚使用，并且有许多复杂的字母。目前在埃塞俄比亚，关于阿姆哈拉语字母手语的研究很少。另一方面，以前的工作只研究了基本的和一些派生的阿姆哈拉语字母手语。为了解决这一挑战，在本文中，我们提出了机器学习技术，如支持向量机（SVM）与卷积神经网络（CNN）、方向梯度直方图（HOG）及其混合特征，以识别其余派生的阿姆哈拉语字母手语。由于CNN对手语的旋转和平移效果良好，而HOG在强光照变化和少量训练数据下对低质量数据效果良好，因此将两者结合用于特征提取。除了SVM之外，CNN（Softmax）还被用作归一化混合特征的分类器。使用10折交叉验证，使用CNN、HOG、归一化和非归一化混合特征向量的SVM模型分别达到了89.02%、95.42%、97.40%和93.61%的准确率。对于归一化混合特征，另一个分类器CNN（sofmax）的准确率为93.55%。
