Browsing by Author "Fernando, K.S.S."

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    Sri Lankan Sign Language Tutor
    (Uva Wellassa University of Sri Lanka, 2013) Fernando, K.S.S.
    Sign Language Recognition is a challenging research area of Human Computer Interaction. This system proposes a method which recognizes signs of Sri Lankan Sign Language using Fourier Transformation, which is invariant to translation, scaling, rotation and change of starting point. It discusses about using a Centroid distance based shape signature, which is capable of preserving both local and global information of the shape. This concept would be highly beneficial for primary school students who try to learn the basics of sign language. This system will help them to practice & check their knowledge without any help of their teachers or parents. Digital Image Processing Techniques were used to obtain a closed contour image from the input image. Feature Extraction is done by using the theories of Fourier Transformation. Artificial Neural Network has been employed to train a large set of signs in order to increase the efficiency of the system. Supervised training method was used to train the neural network, which consists of 10 input nodes, 6 hidden layer nodes and 8 output nodes. The calculated weights were stored in file. The system is implemented using C# programming language and Aforge.NET framework. A still image of the sign is taken as the input for the system. The weight file, which is generated at the end of training the system for nearly 800 images of signs, was used to recognize the sign. The system will output the correctness of the sign to the user using visual indicators. The system is capable of recognizing 8 static signs of Sri Lankan Sign Language successfully.
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    Sri Lankan Sign Language Tutor
    (Uva Wellassa University of Sri Lanka, 2013) Fernando, K.S.S.; Wickramarathne, S.D.H.S.
    Sign Language Recognition is one of the major research areas of Human Computer Interaction (HCI). A large number of researches had been done in this area for American Sign Language, Indian sign language, Chinese sign language, Thai sign language, etc. (Rajathi et al., 2013). But, less amount of researches are done related to Sinhala sign language recognition. Specially no research work found in developing a tutor for Sinhala sign language. There may have problems when teaching sign language to disabled children due to lack of teachers, less/no attention to every child at every moment due to lack of resources, parents of these disabled children may be too busy, less interest of children to study, etc. Therefore, this research was carried out to develop an automated tutor to the Sri Lankan deaf community for Sinhala sign language to practice & check their knowledge. Methodology This is an Image based Sign Recognition System which uses Fourier Descriptors for feature Extraction (Nixon et al., 2002). The system architecture can be divided into five modules as shown in Figure 1. In Image Acquisition Module, images of 200*200dpi (dots per pixel) resolution which contains only the hand was captured in a black background by wearing a black long sleeve top with a white glove. In Image Processing Module, image processing techniques will be applied to manipulate the image. It results the pure contour of the shape as the final output. In Feature Extraction Module, Fourier Transformation is applied on closed contour. For that the shape contour is sampled to 64 points by equal arc length method and Centroid distance function is applied. Discrete Fourier Transformation is applied to the Centroid distance shape signature which result 32 Fourier Descriptors. The Fourier Descriptors were modified in order to preserve scale and rotation invariance by dividing them by the first Fourier Descriptor. The first 10 Fourier Descriptors except the first Fourier Descriptor are taken into account. In Neural Network Module, a neural network of 10 input layers, 6 output layers and 8 output layers is used. The 10 Fourier Descriptors of each sign are fed as the input and a pattern for each sign was fed as the output. It will output the results as a neuron weight file. This weight file is used in Result Module to determine the correctness (right/wrong) of each sign to the user. The result will be displayed to the user by a two visual indicators. Result and Discussion Sri Lankan Sign Language Tutor was implemented to recognize the correctness of 8 static signs of Sinhala Alphabet (Figure 2). The system is tested with 800 images of signs including 100 images from each sign for scale, rotation, translation and starting point invariance and the obtained accuracy level for each letter is listed in Table 1. The system is trained by using 200 images of signs including 25 images from each sign. The obtained accuracy level can be increased by increasing the training set. But it requires more time.