Sign Language
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Aniket Wattamwar

Abstract: This research work presents a prototype system that helps to recognize hand gesture to normal people in order to communicate more effectively with the special people. Aforesaid research work focuses on the problem of gesture recognition in real time that sign language used by the community of deaf people. The problem addressed is based on Digital Image Processing using CNN (Convolutional Neural Networks), Skin Detection and Image Segmentation techniques. This system recognizes gestures of ASL (American Sign Language) including the alphabet and a subset of its words. Keywords: gesture recognition, digital image processing, CNN (Convolutional Neural Networks), image segmentation, ASL (American Sign Language), alphabet

2021 ◽  
Vol 95 ◽  
pp. 107395
Wadood Abdul ◽  
Mansour Alsulaiman ◽  
Syed Umar Amin ◽  
Mohammed Faisal ◽  
Ghulam Muhammad ◽  

2021 ◽  
Vol 12 (1) ◽  
Feng Wen ◽  
Zixuan Zhang ◽  
Tianyiyi He ◽  
Chengkuo Lee

AbstractSign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers’ daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers.

Katharina Hartmann ◽  
Roland Pfau ◽  
Iris Legeland

This paper investigates coordination in Sign Language of the Netherlands (NGT). We offer an account for a typologically unusual coordination pattern found in this language. We show that the conjuncts of a coordinated structure in NGT may violate a constraint governing coordinated structures in spoken languages, which we refer to as the ‘Parallel Structure Constraint’. The violation consists in asymmetric topicalization to SpecTopP or SpecFocP in the second conjunct of a coordinated structure. We argue that a PSC violation is acceptable in NGT in order to express a contrast across the conjuncts, hence asymmetric reordering in the second conjunct yields the desired strength of marking.

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