Sign language is a natural language mostly used by persons with hearing- and speech-based impairments to communicate with other people. In these modern times, sign language guides are used to eliminate the communication gap between people having hearing impairments and those with or without hearing impairments; however, they are very limited in number. To solve this challenge, automatic sign language recognition systems are developed to better reduce the communication gap for people with hearing disabilities. This paper presents the development of an automatic Amharic sign language translator which translates Amharic alphabet signs into their corresponding text using digital image processing and machine learning algorithms. The proposed system has four major developmental stages which include preprocessing, segmentation, feature extraction and classification. A total number of thirty-four features were extracted from shape, motion and color of hand gestures to represent both the base and derived class of Amharic sign characters. Classification models were built using artificial neural network (ANN) and multi-class support vector machine (SVM). The results show that the recognition system is capable of recognizing the Amharic alphabet signs with an average accuracy of 80.82% and 98.06% using the ANN and SVM classifiers, respectively.

手语是一种自然语言，通常被听力和言语障碍人士用来与其他人交流。在当今时代，手语指南用于消除听力障碍者与有或没有听力障碍者之间的沟通差距；但是，它们的数量非常有限。为了解决这一挑战，开发了自动手语识别系统，以更好地减少听力障碍者的沟通差距。本文介绍了一种自动阿姆哈拉语手语翻译器的开发，该翻译器使用数字图像处理和机器学习算法将阿姆哈拉语手语翻译成相应的文本。拟议的系统有四个主要的开发阶段，包括预处理，分段，特征提取和分类。从手势的形状，运动和颜色中提取了总共34个特征，以表示阿姆哈拉语符号字符的基础和派生类。分类模型是使用人工神经网络（ANN）和多类支持向量机（SVM）建立的。结果表明，该识别系统能够使用ANN和SVM分类器分别识别Amharic字母符号，其平均准确度分别为80.82％和98.06％。