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Sign Language Recognition (SLR) has gained reasonable interest by the researches in the last decade. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98%.īackground: Sign Language is one of the major application of Assistive Technology (AT).
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Mean while, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book.
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Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. The experimental results illustrated that the proposed system has a recognition accuracy over 90%Ĭomputer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in real-time from the camera device. We get in our consideration that each word may has a different occlusion state. A gesture dataset of 40-segmented words that frequently used in the life by the hearing-impaired person was utilized for testing the system.
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The computerized system will be learned with gestures and its corresponding spoken and written Arabic words. The segmented gestures are classified and stored in an electronic library after they have been recognized and linked with their corresponding spoken and written Arabic words. In this project, we will try to build a computerized system depending on the depth-measuring cameras and computer vision techniques to capturing and segmenting the pictures of the facial expressions and hand gestures. The disabled person uses facial expressions and hand gestures to express his needs. The most famous of these methods is the sign language which is relying on the body language. Therefore, the owners of this disability attempt to break this barrier by using many methods to try to communicate with others. This disability creates a barrier that prevents the disabled from integrating with society. The deaf-mutes person suffers from inability to speak as well as inability to hear others. We are building an application which will change the voice of the client over to communication through signing with the assistance of normal language preparing semantics. Our venture expects to overcome any barrier between these both discourse disabled and hearing-weakened individuals and ordinary individuals with the coming of new innovations or technologies.
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To the extent both discourse disabled and hearing-weakened individuals are concerned, approaching gesture based communication is extremely fundamental for their social, passionate, etymological and social development of people. Individuals experiencing hearing and communicating in handicaps utilize communication through gestures as a direct method of correspondence among one another and furthermore with others yet tragically few out of every odd one of the ordinary individuals can grasp communication through gestures consequently it brings about an absence of correspondence and isolation. Gesture based communication is a visual language used by both discourse disabled and hearing-impeded individuals as their first language.