Abstract:
In the day-to-day life of communities, good communication channels are crucial for mutual understanding. e hearing-impaired
community uses sign language, which is a visual and gestural language. In terms of orientation and expression, it is separate from
written and spoken languages. Despite the fact that sign language is an excellent platform for communication among hearing impaired persons, it has created a communication barrier between hearing-impaired and non-disabled people. To address this
issue, researchers have proposed sign language to text translation systems for English and other European languages as a solution.
e goal of this research is to design and develop an Amharic digital text converter system using Ethiopian sign language. e
proposed system was created with the help of two key deep learning algorithms: a pretrained deep learning model and a Long
Short-Term Memory (LSTM). e LSTM was used to extract sequence information from a sequence of image frames of a speci c
sign language, while the pretrained deep learning model was used to extract features from single frame images. e dataset used to
train the algorithms was gathered in video format from Addis Ababa University. Prior to feeding the obtained dataset to the deep
learning models, data preprocessing activities such as cleaning and video to image frame segmentation were conducted. e
system was trained, validated, and tested using 80%, 10%, and 10% of the 2475 images created during the preprocessing step. Two
pretrained deep learning models, E cientNetB0 and ResNet50, were used in this investigation, and they attained an accuracy of
72.79%. In terms of precision and f1-score, ResNet50 outperformed E cientNetB0. For the proposed system, a graphical user
interface prototype was created, and the best performing model was chosen and implemented. e proposed system can be
utilized as a starting point for other researchers to improve upon, based on the outcomes of the experiment. More high-quality
training datasets and high-performance training machines, such as GPU-enabled computers, can be added to the system to
improve it
