Amharic Phrase Level Sign Language Recognition Using Deep Learning

Teferi, Girma Messele

dc.contributor.author	Teferi, Girma Messele
dc.date.accessioned	2021-09-29T11:37:44Z
dc.date.available	2021-09-29T11:37:44Z
dc.date.issued	2021-07
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/12667
dc.description.abstract	Sign language is communication between hearing-impaired with hearing-impaired and hearing-impaired with normal people. It is also a way to communicate using hand and face gestures of phrases, words, or letters and it is a nonverbal communication language. However, there is a gap in communication between hearing-impaired with hearing peoples. This is due to the reason that most hearing people in Ethiopia do not know and understand the sign. So, a study aims to design a model that converts Ethiopian Amharic phrase signs to text to eliminate the communication gaps between the hearings disabled with hearing people because there is adifficulty of communication with Amharic phrase level that could not solve the previous works. We used deep learning techniques to solve a problem and achieve the objectives. Hybrid networks (CNN with LSTM) are applied in our study. Because a hybrid network is used to increase the performance of the model. For feature extraction, we used a convolutional neural network (CNN) algorithm and for classification, we used Long-short-term-memory (LSTM). LSTM has an ability to classify sequences of information and remember it for a long period of time. From the total dataset of 10500, 80% (8400) used for training, and 10% (1050) used for validation, and 10%(1050) also used for testing the model. The testing accuracy of the model is 96%. We used accuracy, precision, recall, f1-score, and confusion matrix to evaluate our model.	en_US
dc.language.iso	en_US	en_US
dc.subject	INFORMATION TECHNOLOGY	en_US
dc.title	Amharic Phrase Level Sign Language Recognition Using Deep Learning	en_US
dc.type	Thesis	en_US