Sign language learning based on Android for deaf and speech impaired people

Setiawardhana,; Hakkun, Rizky Yuniar; Baharuddin, Achmad

doi:10.1109/elecsym.2015.7380825

Cited by 26 publications

(10 citation statements)

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“…In [5] Object detection is achieved using Viola-Jones Algorithm, which is an algorithm that detects objects that are in the form of a hand [6]. The Viola-Jones algorithm detects the object using feature value which is faster than the detection based on the value per pixel of an image.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Sign Language Detection

Purkayastha¹,

Jyoti²,

Pradhan³

et al. 2018

IJCA

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The most important aspect of a society is communication. Every culture in the world has their own way of communicating with people of their kind. But the people who cannot hear or speak cannot easily share their thoughts. This creates a sort of isolation for people like them. The hand gestures that people of special abilities make to communicate would be taken up from a camera and the same would be translated and show on the screen along with an audible source. The most basic methods one has to follow in most of the sign language detection system are tracking, edge detection, segmentation, and a dataset. The first and foremost part is the recognition of hand or motion tracking of the hand from the camera. After the hand is successfully detected the extra environment must be diminished. The removal of an extra environment is done with the help of edge detection and segmentation through various image processing algorithms. After the distinguished picture is obtained, with the help of machine learning techniques and neural networks the result obtained are compared to the existing normalized dataset which is then translated over and produced as an output.

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Section: Related Workmentioning

confidence: 99%

A Survey on Sign Language Detection

Purkayastha¹,

Jyoti²,

Pradhan³

et al. 2018

IJCA

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“…Research in the area of systems that are capable of recognising sign language has received substantial attention over the past few decades, fuelled in particular by the rapid evolution of artificial intelligence techniques [1][2][3]. In turn this has led to the development of many Sign Language Recognition Systems (SLR), which shall be referred to as SLR systems throughout the remainder of this chapter.…”

Section: Introductionmentioning

confidence: 99%

“…These systems though varying in sign language dialect, share the common goal of correctly recognising hand gestures performed by a signer. However the varying proposed approaches to achieving this goal has produced a diverse area of research and development encompassing areas of computer science such as Computer Vision (CV), Sensor Processing, Human-Computer Interaction, and Pattern Recognition [1][2][3][4]. Of these SLR systems there are two main types of design and implementation, which are those that use wearable sensors, and those that use video footage and images.…”

Section: Introductionmentioning

confidence: 99%

A CNN sign language recognition system with single & double-handed gestures

Buckley

Sherrett

Secco

2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

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This work aims at presenting a novel Computer Vision approach in the development of a real-time, web-camera based, British Sign Language recognition system. A literature review focused on current (1) state of sign language recognition systems and (2) techniques used is conducted. This review process is used as a foundation on which a Convolutional Neural Network (CNN) based system is designed and then implemented. A bespoke British Sign Language datasetcontaining 11,875 images -is then performed to train and test the CNN which is used for the classification of human hand performed gestures. Finally, the CNN architecture recognized 19 static British Sign Language gestures, incorporating both single and double-handed gestures. During testing, the system achieved an average recognition accuracy of 89%.

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“…The main purpose to present this work is to facilitate hearing-impaired persons [6] and reduce the communication gap between hearing-impaired and normal individuals of the society [7]. According to published work, it was recorded that minimum duration to identify a gestures is 6-20 fps [8].…”

Section: Introductionmentioning

confidence: 99%

Communicator for Hearing-Impaired Persons using Pakistan Sign Language (PSL)

Wasim¹,

Siddiqui²,

Shaikh³

et al. 2018

ijacsa

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Communication with a hearing-impaired individual is a big challenge for a normal person. Hearingimpaired people uses hand gesture language (sign language) to communicate with each other, which is not easy to understand by a normal person because he/she is not trained to understand sign language. This communication gap between a hearing-impaired and a normal person created big problem for hearing-impaired individuals during their shopping, hospitalization, at their schools and homes. Especially in case of emergency, it is very difficult to understand the statement of a hearing-impaired one's who uses sign language. In the last few years researchers and developers from all over the world presented different ideas and works to solve this problem but no such solution is available to resolve this issue and can create two-way communication between hearing-impaired and normal persons. This paper presented a detail description about a two-way communication system based on Pakistan Sign Language (PSL). This duplex system is developed through conversion from the text in simple English into hand gestures and vice versa. However, conversion from hand gestures is available not only in text but also with voice providing more convenience to normal person. Main objective is to facilitate a large population and making hearingimpaired persons, the vital part of our civilization. A normal person can enter the text (sentence) in application, after the checking of spelling and grammar, the text is divided into tokens and sub-tokens. A token is a gesture against each word of the text while sub-tokens are the gestures of each character of the words. The combination of tokens created the gestures of text. On the other hand when gestures were input in to the application, using image processing technique, the nature of hand gesture were recognized and converted into corresponding text or voice.

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Sign language learning based on Android for deaf and speech impaired people

Cited by 26 publications

References 0 publications

A Survey on Sign Language Detection

A Survey on Sign Language Detection

A CNN sign language recognition system with single & double-handed gestures

Communicator for Hearing-Impaired Persons using Pakistan Sign Language (PSL)

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