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

Manisha U. Kakde, Mahender G. Nakrani, Amit M. Rawate
2016 International Journal of Engineering Research and  
The advancement in embedded systems, provides a space to design and develop a sign language translator system to assist the dumb people, there exist a number of assistant tools.  ...  This paper reviews a different methods adopted to reduce barrier of communication by developing an assistive device for deaf-mute persons.  ...  To recognize that language two recurrent neural networks are used i.e. Partial recurrent network and fully recurrent network. In this, input image was captured through digital camera.  ... 
doi:10.17577/ijertv5is031036 fatcat:22zr5mbeojhxlbjupp6pdhzuj4


2013 Journal of Computer Science  
In order to improve recognition accuracy, researchers use methods, such as the hidden Markov model, artificial neural networks and dynamic time warping.  ...  Some examples are American Sign Language (ASL), Chinese Sign Language (CSL), British Sign Language (BSL), Indonesian Sign Language (ISL) and so on.  ...  The results showed that the fully recurrent neural network system (with recognition rate 95.11%) is better than the Elman neural network (89.67%).  ... 
doi:10.3844/jcssp.2013.1496.1505 fatcat:ul4esnigrze2to6yxftwatcjai

Gesture Based Real-time Indian Sign Language Interpreter

Akshay Divkar, Rushikesh Bailkar, Dr. Chhaya S. Pawar
2021 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
Convolutional Neural Network and Recurrent Neural Network will produce the text output of the respective gesture.  ...  Hand gesture is one of the methods used in sign language for non-verbal communication.  ...  Recurrent Neural Network The sequence itself has the information, and recurrent neural networks (RNNs) use this for the recognition tasks.  ... 
doi:10.32628/cseit217374 fatcat:ykfhmldqabbihk5jr4uwjnd644

Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network

Linchu Yang, Jian Chen, Weihang Zhu
2020 Sensors  
The proposed system has been tested on the American Sign Language (ASL) datasets with 360 samples and 480 samples, and the Handicraft-Gesture dataset, respectively.  ...  In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the recognition of dynamic hand gestures from a Leap Motion  ...  Avola et al. presented a deep Recurrent Neural Network model to identify dynamic hand gestures from American Sign Language [23] .  ... 
doi:10.3390/s20072106 pmid:32276493 pmcid:PMC7180537 fatcat:ckoliffd4nba3aii76rp2qymum

Dilated Convolutional Network with Iterative Optimization for Continuous Sign Language Recognition

Junfu Pu, Wengang Zhou, Houqiang Li
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition.  ...  Experimental results on RWTH-PHOENIX-Weather, a large real-world continuous sign language recognition benchmark, demonstrate the advantages and effectiveness of our proposed method.  ...  The large datasets such as RWTH-PHOENIX-Weather make it possible to train deep neural networks for sign language recognition.  ... 
doi:10.24963/ijcai.2018/123 dblp:conf/ijcai/PuZL18 fatcat:zoqolvteovhf3ovwg4wzsm4wcq

Fingerspelling Identification For American Sign Language Based On Resnet-18

ZHANG Han-wen, HU Ying, ZOU Yong-jia, WU Cheng-yu
2021 International journal of advanced networking and applications  
Aiming at the small number of sign language samples and low detection accuracy, an American sign language detection method based on Resnet-18 and data augmentation is proposed.  ...  With the rapid development of the field of deep learning, the field of sign language recognition has ushered in new opportunities.  ...  ACKNOWLEDGEMENTS This study was supported by 2019 National College Student innovation and entrepreneurship training program project "Research and implementation of sign language recognition system based  ... 
doi:10.35444/ijana.2021.13102 fatcat:y6ch5wjvungkvi7zghjfsfee2i

Convolutional and Recurrent Neural Network for Human Action Recognition: application on American Sign Language [article]

Vincent Hernandez, Tomoya Suzuki, Gentiane Venture
2019 bioRxiv   pre-print
This study proposes to classify 60 American Sign Language signs from data provided by the LeapMotion sensor by using a combined approach with Convolutional Neural Network (ConvNet) and Recurrent Neural  ...  Network with Long-Short Term Memory cells (LSTM) called ConvNet-LSTM.  ...  the first to combined approach with Convolutional Neural Network 345 and a Recurrent Neural Network with Long-Short Term Memory cells (ConvNet-LSTM) for 346 sign language recognition.  ... 
doi:10.1101/535492 fatcat:p6vvgsbfpvbmnfjkyejl2rm7p4

Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison [article]

Dongxu Li, Cristian Rodriguez Opazo, Xin Yu, Hongdong Li
2020 arXiv   pre-print
Vision-based sign language recognition aims at helping deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words.  ...  Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios.  ...  Acknowledgement This research is supported in part by the Australia Research Council ARC Centre of Excellence for Robotics Vision (CE140100016), ARC-Discovery (DP 190102261) and ARC-LIEF (190100080).  ... 
arXiv:1910.11006v2 fatcat:awlrk3qm3jeqphw4e7bu3srf6m

DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition

Saleh Aly, Walaa Aly
2020 IEEE Access  
and deep recurrent neural network.  ...  Traditional sign language recognition methods utilize color-based hand segmentation algorithms to segment hands, hand-crafted feature extraction for hand shape representation and Hidden Markov Model (HMM  ...  The performance of the proposed method outperforms all state-of-the-art methods and can be extended to solve continuous sign language recognition problem for Arabic and other languages.  ... 
doi:10.1109/access.2020.2990699 fatcat:rnov5ewiprdcrh2jf5h6em626q

Convolutional and recurrent neural network for human activity recognition: Application on American sign language

Vincent Hernandez, Tomoya Suzuki, Gentiane Venture, Jie Zhang
2020 PLoS ONE  
DeepConvLSTM and convolutional neural network demonstrated the highest accuracy compared to other models with 91.1 (3.8) and 89.3 (4.0) % respectively compared to the recurrent neural network or multi-layer  ...  Integrating convolutional layers in a deep learning model seems to be an appropriate solution for sign language recognition with depth sensors data.  ...  Nevertheless, a static neural network may still be considered with an automatic segmentation method and then interpolate the signal.  ... 
doi:10.1371/journal.pone.0228869 pmid:32074124 pmcid:PMC7029868 fatcat:jvs2oxvenvg4rjoz4joyo6zgay

FineHand: Learning Hand Shapes for American Sign Language Recognition [article]

Al Amin Hosain, Panneer Selvam Santhalingam, Parth Pathak, Huzefa Rangwala, Jana Kosecka
2020 arXiv   pre-print
American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures.  ...  For hand shape recognition our method uses a mix of manually labelled hand shapes and high confidence predictions to train deep convolutional neural network (CNN).  ...  Our proposed method is RGB only but outperforms multi-modal (RGB and pose) approaches of sign language recognition.  ... 
arXiv:2003.08753v1 fatcat:arbaaf2ezfh7nkgieemsofsyee

American Sign Language Recognition using Deep Learning and Computer Vision

Kshitij Bantupalli, Ying Xie
2018 2018 IEEE International Conference on Big Data (Big Data)  
The authors of [8] worked on American Sign Language with a custom dataset of their own making.  ...  Vivek [8] developed a model for American Sign Language recognition consisting of a custom CNN model consisting of 6 convolutional layers with a dropout of 0.25. and a final dropout layer of dropout 0.5  ... 
doi:10.1109/bigdata.2018.8622141 dblp:conf/bigdataconf/BantupalliX18 fatcat:qbh4hahgunhltihzpvofpngwge

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Manju Khari, Aditya Kumar Garg, Rubén Gonzalez-Crespo, Elena Verdú
2019 International Journal of Interactive Multimedia and Artificial Intelligence  
Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model.  ...  Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task.  ...  The main contribution of this paper is to propose a Neural Network that will help in increasing the recognition rate on American Sign Language Dataset.  ... 
doi:10.9781/ijimai.2019.09.002 fatcat:7cechyexx5apljwsghxabwa5jm

Recognition of Static Hand Gestures of Alphabet in Bangla Sign Language

Md. Atiqur Rahman
2012 IOSR Journal of Computer Engineering  
A BSL finger spelling and an alphabet gesture recognition system was designed with Artificial Neural Network (ANN) and constructed in order to translate the BSL alphabet into the corresponding printed  ...  This paper presents a system for recognizing static hand gestures of alphabet in Bangla Sign Language (BSL).  ...  Murakami and Taguchi [3] investigated the use of recurrent neural nets for Japanese Sign Language recognition.  ... 
doi:10.9790/0661/0810713 fatcat:xw76rfffsnclrbwqecttlhayqi

BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using CNN and LSTM

Andi Aljabar, Suharjito Suharjito
2020 Advances in Science, Technology and Engineering Systems  
In this model, there are a lot of methods such as convolutional neural network, recurrent neural network, long-sort term memory, and each model has its characteristics.  ...  There are also some issues in deep learning by sign language recognition as the object such as data training, object position, pose, lighting, and the background of objects.  ...  Although this paper point explains the deferential between CNN, LSTM, and CNN+LSTM models, the dataset needs to add for all symbolic in sign language.  ... 
doi:10.25046/aj050535 fatcat:gf37eyzoszfz3dmjtadaklxdfy
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