Word Level Amharic Sign Language Recognition using Deep Learning Algorithms
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Date
2022-02
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Addis Ababa University
Abstract
In Ethiopia, Deaf peoples are vastly increase in number. Sign language is a natural language
mostly used by Deaf persons to communicate with each other. However, during communication
there is a big challenge between Deaf and normal person. Deaf use sign for communication
whereas normal person use speech/text for communication.
We need efficient system to exchange sign to speech/text or speech/text to sign. This thesis work
focus on development of word level Amharic sign language recognition, translates Amharic
word sign into their corresponding Amharic text using deep learning approach. The input for the
system is video frames of Amharic sign words and the final output of the system is Amharic text.
The proposed system has three major components: preprocessing, feature extraction and
classification. Two preprocessing steeps were used, cropping and RGB to Grayscale conversion.
Feature extraction was done by using deep residual network (ResNet-34) and store in .csv
format.
Finally, classification was done by the same deep learning algorithms ResNet-34. The system is
trained and tested using a dataset prepared for this thesis purpose only for all Amharic sign
words. The performance of the model measured by four different matrices (precision, recall, F1
score and accuracy).
The system classify 60 sign words and score overall accuracy of 95%. Therefore, the
classification performance of ResNet-34 is very good.
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Keywords
Amharic sign words, deep learning algorithms, ResNet-34