A Review Paper on Sign Language Recognition System For Deaf And Dumb People using Image Processing

Kakde, Manisha U.; Nakrani, Mahender G.; Rawate, Amit M.

doi:10.17577/ijertv5is031036

Cited by 10 publications

(5 citation statements)

References 11 publications

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Section: Sign Language Recognition Systemmentioning

confidence: 99%

“…A special issue from Reference 24 mainly focuses on recent state of art and technological advancements and scientific issues relating to the sign language recognition system. Kakde et al in his approach, 25 reviews different techniques of SLR based on Sign acquiring and identification methods. Various techniques to develop a prototype for SLR systems are discussed in Reference 26.…”

Section: Motivations and Problem Statementmentioning

confidence: 99%

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A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Robert

Hemanth

2023

Concurrency and Computation

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Summary The recent advancements in computer vision and deep learning have led to promising progress in various motion detection and gesture recognition methods. Thriving efforts in the field of sign language recognition (SLR) during recent years led to interaction between humans and computer systems. Contributing a real‐time automated Sign Language recognition system will be a remarkable treasure for hearing and speech‐impaired people that will break the barriers of interaction with the real world. Albeit several research works are accomplished in sign language recognition, there is still a demand for developing a real‐time automated sign language recognition system. Compared to other methodologies, the techniques adopted may have advantages and disadvantages, and they vary from researcher to researcher. There are still some issues with employing these SLR models and procedures regularly, even though numerous research studies have been conducted to determine the most acceptable methods and models for sign language recognition. It gets more expensive and difficult in terms of resources when the developed automated SLR system becomes available as a product. Hence, for the welfare of the hearing and speech‐impaired community, the researchers are still endeavoring way to find a cost‐effective method. This work brings forth the challenges faced by scientists in developing a cost‐effective commercial prototype for the hearing and speech‐impaired community. This paper also explores and analyses various deep learning techniques and methods used in developing a sign language recognition system. The objective behind this work is to identify the best method which produces high accuracy in developing a cost‐effective sign language recognition system aiding communication between signers and non‐signers so that they can be a part of growing technology.

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Section: Sign Language Recognition Systemmentioning

confidence: 99%

Section: Motivations and Problem Statementmentioning

confidence: 99%

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Robert

Hemanth

2023

Concurrency and Computation

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“…This dataset was distributed into a set of test and train data. This data set was trained using recurrent neural network (RNN) architecture called LSTM which consisted of 3 LSTM layers and 3 dense layers as shown in Figure 2 [17], [18]. The categorical Accuracy of the model is shown in the form of a graph in Figure 3 and the model was trained for around 2000 Epochs.…”

Section: Figure 1 Static Sign Language Translationmentioning

confidence: 99%

“…Real-time dynamic sign language translation shown in Figure 6 was performed using open CV2 and ML that was integrated with Kinect. [16], [18]- [22] Urdu audio to Urdu text translation is performed using the google speech App which performs complete translation of all the letters, Words, and Sentences as shown in Figure 7. The text gathered from sign language is then converted into audio using Google Text to Speech App which helps in converting Urdu text into Urdu audio and a complete system of PSL to Urdu Translator is formed as shown in Figure 8.…”

Section: Figure 1 Static Sign Language Translationmentioning

confidence: 99%

Pakistan sign language to Urdu translator using Kinect

Ahmed

Shafiq

Raheel

et al. 2022

Comput Sci Inf Technol

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The lack of a standardized sign language, and the inability to communicate with the hearing community through sign language, are the two major issues confronting Pakistan's deaf and dumb society. In this research, we have proposed an approach to help eradicate one of the issues. Now, using the proposed framework, the deaf community can communicate with normal people. The purpose of this work is to reduce the struggles of hearing-impaired people in Pakistan. A Kinect-based Pakistan sign language (PSL) to Urdu language translator is being developed to accomplish this. The system’s dynamic sign language segment works in three phases: acquiring key points from the dataset, training a long short-term memory (LSTM) model, and making real-time predictions using sequences through openCV integrated with the Kinect device. The system’s static sign language segment works in three phases: acquiring an image-based dataset, training a model garden, and making real-time predictions using openCV integrated with the Kinect device. It also allows the hearing user to input Urdu audio to the Kinect microphone. The proposed sign language translator can detect and predict the PSL performed in front of the Kinect device and produce translations in Urdu.

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“…It can be useful in case of an emergency too [3]. Sign Language is described as a system of communication which uses visual signs and gestures, as used by deaf people [4].Sign language detection system is basically a system which can detect a particular letter/word/sentence through an image/video input [5]. It can aid in filling the communication void between a person who knows sign language and a normal person who doesn't know sign language.…”

Section: Introductionmentioning

confidence: 99%

Inflated 3D ConvNet for detection of sign language

Chandana,

Tushara,

Ramya Sri

et al. 2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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Every human has their own kind of disabilities, we all try to live and overcome them in our life. We educate ourselves to overcome them, we invent technology to achieve our goals. Sign Language is a communication path for deaf-mute people through hand gestures and actions. Sign Language helps people who can't speak sign language to interact with the people who can speak sign language, this deep learning paper aims to help build a communication bridge for this reason. We used Amazon Rekognition service which uses Deep CNN algorithm for the detection of static images of the signs. As most of the signs are for words they are in the form of videos. We used the I3D algorithm for the classification of videos of the signs of words. The PyTorch framework provides support for CuDNN (NVIDIA CUDA Deep Neural Network) which provides fast GPU implementations for the deep neural networks. The Experimental results has shown that the models used has displayed good results in detecting the words.

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A Review Paper on Sign Language Recognition System For Deaf And Dumb People using Image Processing

Cited by 10 publications

References 11 publications

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Pakistan sign language to Urdu translator using Kinect

Inflated 3D ConvNet for detection of sign language

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