Vision-based sign language translation device

Madhuri, Yellapu; Anitha, G; Anburajan, M.

doi:10.1109/icices.2013.6508395

Cited by 37 publications

(15 citation statements)

References 3 publications

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“…A sign language (also signed language or simply signing) is a language which uses manual communication and body language to convey meaning, as opposed to acoustically conveyed sound patterns [8]. This can involve simultaneously combining hand shapes, orientation and movement of the hands, arms or body, and facial expressions to fluidly express a speaker's thoughts.…”

Section: Sign Recognitionmentioning

confidence: 99%

“…Static gestures represent the alphabets and numeral of a natural language which are arbitrary signs representing specific concepts. Dynamic gestures include the words, sentences, expressions or finger spellings which have the signs representing varying concepts [8]. The first group is the foundation of sign language introduced by the pose of hands called postures, whereas the second group signs include motion of hands, head or both.…”

Section: Introductionmentioning

confidence: 99%

“…Sign language (SL) is the natural way of communication of hearing and/or speech impaired people [8]. Gestures in a sign language are a movement language which is introduced through series of hands and arms motion, facial expressions & head/body postures.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Translation System from Speech Language to Hand Motion Language

Rekha

Latha²

2014

2014 International Conference on Intelligent Computing Applications

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To access information anywhere, advanced technology is needed. Nowadays mobile devices and applications are developing rapidly. Deaf and hard of hearing people find difficult to use mobile devices since they are not able to access information anywhere due to lack of services. They have difficulties with reading and writing, to read and understand all information to use mobile devices due to their hearing impairment which is an invisible disability. The gain of experience collected by deaf children in four years is equivalent to the gain of one year for hearing children. All visual textual information is not accessible for this category of people with disabilities. To provide benefits for deaf people to improve their social integration and communication. In this paper, we propose a solution to this problem by a mobile based sign language translation device for automatic translation of Indian English speech language to sign language. This helps the hearing impaired people to communicate easily with the hearing people. We focus on the relationship between deaf people and the technological progress.

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Section: Sign Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobile Translation System from Speech Language to Hand Motion Language

Rekha

Latha²

2014

2014 International Conference on Intelligent Computing Applications

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“…Cui et al in [7] proposed a CNN with temporal convolution and pooling for spatiotemporal representation learning from a video, and an RNN with a bidirectional Long Short Term Memory (LSTM) for the mapping of feature sequences to sequences of annotations. Madhuri et al in [18], present a real-time vision-based system for recognizing finger spelling continuous Sign Language (SL) using a single camera to track user's unadorned hands. The goal is to help hearing or speech impaired people to communicate with people who do not know SL.…”

Section: From Pose To Gesturementioning

confidence: 99%

“…The goal is to help hearing or speech impaired people to communicate with people who do not know SL. Although facial expressions add relevant information to the emotional aspect of the sign, in [18], they are not considered since their analysis complicates the problem. They only focus on translating a one-handed sign of representation of alphabets (A-Z) and numbers (0-9).…”

Section: From Pose To Gesturementioning

confidence: 99%

AI at the Edge for Sign Language Learning Support

Battistoni

2020

IJHCC

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In the field of multimodal communication, sign language is and continues to be, one of the most understudied areas. Thanks to the recent advances in the field of deep learning, there are far-reaching implications and applications that neural networks can have for sign language mastering. This paper describes a method for ASL alphabet recognition using Convolutional Neural Networks (CNN), which allows to monitor user’s learning progress. American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity in ASL signs, high interclass similarities, large intraclass variations, and constant occlusions. We produced a robust model that classifies letters correctly in a majority of cases. The experimental results encouraged us to investigate the adoption of AI techniques to support learning of a sign language, as a natural language with its own syntax and lexicon. The challenge was to deliver a mobile sign language training solution that users may adopt during their everyday life. To satisfy the indispensable additional computational resources to the locally connected end- user devices, we propose the adoption of a Fog-Computing Architecture.

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