Abstract:
Computer vision based sign language translation is usually based on using thousands of
images or video sequences for model training. This is not an issue in the case of widely
used languages such as American Sign Language. However, in case of languages with
low resources such as Sinhala Sign Language, it’s challenging to use similar methods
for developing translators since there are no known data sets available for such studies.
In this study a sign language translation method is developed using a small data set
for static signs of Sinhala Fingerspelling Alphabet. The classification model is simpler
in comparison to Neural Networks based models which are used in most other sign
language translation systems.
The methodology presented in this study decouples the classification step from
hand pose estimation and uses postural synergies to reduce dimensionality of features.
This enables the model to be successfully trained on a data set as small as 122 images.
As evidenced by the experiments this method can achieve an average accuracy of over
87% . The size of the data set used is less than 12% of the size of data sets used in
methods which have comparable accuracies.
Citation:
Weerasooriya, A. (2022). Classifier for Sinhala fingerspelling sign language [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa.http://dl.lib.uom.lk/handle/123/21905