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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： He, Xiaoran Lin, Yangde Hu, Zhiyuan Xu, Xintong Xu, Rui Xiang, Weiming Tianjin University of Science and Technology Tianjin China

ISBN: (纸本)9798350309614

sign language recognition is an important research direction in the field of computer vision. It is very important to recognize sign language efficiently and accurately to promote the two-way communication between the healthy people and the deaf-mutes so as to help the disabled and other people to integrate into the society harmoniously. There are two types of sign language recognition: Static sign language and dynamic sign language. The former can be recognized by image classification network and other technologies, and it is relatively mature for now. At present, the research in this field is not perfect at home and abroad. In this paper, an integrated sign language recognition system based on Yolov5 target detection algorithm combined with LSTM network and OpenPose technology is proposed, eventually the system will be deployed on the raspberry pie to increase portability. In order to improve the accuracy and efficiency of model recognition, this paper adopts data enhancement algorithm and changes the skeleton, detection head and loss function of Yolov5 model, finally, the model recognition accuracy of 98.87% was obtained on the open source Chinese sign language data set recognition test set of China University of Science and Technology. Our experiments show that our improved model has strong dynamic sign language recognition ability, and we hope that this can provide an improved idea for future sign language recognition, especially dynamic sign language recognition. © 2023 IEEE.

关键词： Chinese sign language data enhancement sign language recognition sign language recognition system target detection

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Vision-based sign language recognition system: A Comprehensive Review 5

Vision-based sign language recognition system: A Comprehensi...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Sharma, Sakshi Singh, Sukhwinder Punjab Engn Coll Elect & Commun Dept Chandigarh India

ISBN: (数字)9781728146850

ISBN: (纸本)9781728146850

Communication is an essential activity for a human being to express their feeling, ideas, work together, and for the overall development of society. A person with a hearing disability uses sign language for communication and this language develops naturally within them. However, it is not commonly understood by non-signer community and hence, there remains a communication gap between these two communities. To bridge this gap, a computer vision based recognition system can be used. Many researchers have been worked on this field to facilitate the ease of communication between these two communities. In this paper, the basic concept of sign language recognition system and review of its existing techniques along with their comparison presented. The main objective of presenting this survey is to highlight the importance of vision based method with a specific focus on sign language.

关键词： Computer vision Image processing sign language sign language recognition system

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ALCTS—An Assistive Learning and Communicative Tool for Speech and Hearing Impaired Students

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Computers, Materials & Continua 2025年第5期83卷 2599-2617页

作者： Shabana Ziyad Puthu Vedu Wafaa A.Ghonaim Naglaa M.Mostafa Pradeep Kumar Singh Computer Science Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj11942Saudi Arabia Faculty of Science Al-Azhar UniversityCairo12111Egypt Department of Mathematics Faculty of ScienceAl-Azhar University(Girl’s Branch)Cairo12111Egypt Department of Computer Science and Engineering Central University of JammuJammu and Kashmir181143India

Hearing and Speech impairment can be congenital or *** and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their ***,the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of *** learning devices enable students to access institutional resources and facilities *** proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and *** tool converts the audio signals into sign language videos for the speech and hearing-impaired to follow and converts the sign language to text format for the teachers to *** educational tool for the speech and hearing-impaired is implemented by customized deep learning models such as Convolution neural networks(CNN),Residual neural Networks(ResNet),and stacked Long short-term memory(LSTM)network *** assistive learning tool is a novel framework that interprets the static and dynamic gesture actions in American sign language(ASL).Such communicative tools empower the speech and hearing impaired to communicate effectively in a classroom environment and foster *** deep learning models were developed and experimentally evaluated with the standard performance *** model exhibits an accuracy of 99.7% for all static gesture classification and 99% for specific vocabulary of gesture action *** two-way communicative and educational tool encourages social inclusion and a promising career for disabled students.

关键词： sign language recognition system ASL dynamic gestures facial key points CNN LSTM ResNet

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A Spatio-Temporal Framework for Dynamic Indian sign language recognition

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2527-2541页

作者： Sharma, Sakshi Singh, Sukhwinder Punjab Engn Coll ECE Dept Chandigarh India

A sign language recognition system is a boon to the signer community as it eases the flow of information between the signer and non-signer communities. However, extracting timely detail from the video data is still a challenging task. In this paper, a deep learning based model consisting of trainable CNN and trainable stacked 2 bidirectional long short term memory (S2B-LSTM) has been proposed and tested to recognise the dynamic gestures of Indian sign language (ISL). The CNN architecture has been used as feature extractor to extract the spatial features from the input video data, whereas the temporal relation between the consecutive frames of input video is extracted using S2B-LSTM. This model has been trained and tested on self-developed dataset consisting of 360 videos of ISL dynamic gestures. The CNN-S2B-LSTM model outperforms the existing techniques of sign language recognition with best recognition accuracy of 97.6%.

关键词： Convolutional neural network Deep learning Long short-term memory Indian sign language sign language recognition system

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Gesture recognition of Filipino sign language Using Convolutional and Long-Short Term Memory Neural Network 1

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Intelligent systems Conference (IntelliSys)

作者： Cayme, Karl Jensen F. Retutal, Vince Andrei B. Salubre, Miguel Edwin P. Canete, Luis Gerardo S. Astillo, Philip Virgil B. Univ San Carlos Cebu Philippines

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

sign language is a form of communication prominently used by the deaf-mute community to convey their ideas and thoughts. In the Philippines, local signers use Filipino sign language (FSL) derived from the well-known American sign language (ASL). Despite the recent formalization of FSL as the country's official sign language, there is still minimal familiarity among the public. That said, sign language recognition (SLR) systems integrated with machine learning applications have been developed to understand FSL better. However, the prevalent limitations of most of these systems are that it only involves static signs and asynchronous recognition. This study aimed to take this solution further and overcome existing limitations by developing a model capable of recognizing FSL gestures in real-time usable for applications such as in government service centers. To this end, the study proposes the deep learning algorithm Convolutional and Long Short-Term Memory Neural Networks in system capturing of real-time signs from a signer. The proponents considered 15 signs related to common greetings and business transactions. A total of 450 video recordings were collected for the signs with each having an equal number of samples. The collected data underwent cleaning, preprocessing, and augmentation before training. The proposed model's performance was analyzed with the following classification metrics: Accuracy, Precision, Recall, and F1-Score, and was able to achieve 95% accuracy and a macro-average of 0.95 precision, 0.95 Recall, and 0.95 F1-Score. Furthermore, the model had a comparable accuracy and loss between validation and test data-a 95.18% accuracy and 0.13629 loss on validation while 95.93% accuracy and loss of 0.1478 on the test. With that said, the proposed model was well-fit for classifying the 15 signs that involve upper body movements.

关键词： Computer vision CNN-LSTM Deep learning Filipino sign language Image processing sign language recognition system Machine learning

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recognition of Indian sign language (ISL) Using Deep Learning Model

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期123卷 671-692页

作者： Sharma, Sakshi Singh, Sukhwinder Punjab Engn Coll Deemed Univ ECE Dept Chandigarh India

An efficient sign language recognition system (SLRS) can recognize the gestures of sign language to ease the communication between the signer and non-signer community. In this paper, a computer-vision based SLRS using a deep learning technique has been proposed. This study has primary three contributions: first, a large dataset of Indian sign language (ISL) has been created using 65 different users in an uncontrolled environment. Second, the intra-class variance in dataset has been increased using augmentation to improve the generalization ability of the proposed work. Three additional copies for each training image are generated in this paper, by using three different affine transformations. Third, a novel and robust model using Convolutional Neural Network (CNN) have been proposed for the feature extraction and classification of ISL gestures. The performance of this method is evaluated on a self-collected ISL dataset and publicly available dataset of ASL. For this total of three datasets have been used and the achieved accuracy is 92.43, 88.01, and 99.52%. The efficiency of this method has been also evaluated in terms of precision, recall, f-score, and time consumed by the system. The results indicate that the proposed method shows encouraging performance compared with existing work.

关键词： Indian sign language sign language recognition system Gesture recognition Convolutional neural network Deep learning Data augmentation

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ASL Hand Gesture Classification and Localization Using Deep Ensemble Neural Network

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IEEE ACCESS 2022年第5期10卷 6689-6702页

作者： Barbhuiya, Abul Abbas Karsh, Ram Kumar Jain, Rahul Natl Inst Technol Silchar Dept ECE Cachar 788010 India

The growing number of deaf and hearing-impaired populations and the evolution of vision-based application devices present enormous opportunities for research and modelling in the domain of gesture classification and recognition. Since hand gesture recognition is crucial in sign language interpretation, an efficient gesture recognition system should consider maximum sign characters for recognition. In this work, a robust hand gesture recognition model is proposed by employing deep ensemble neural networks. Initially, a pre-trained network is designed by adopting the VGG16 architecture with a self-attention layer embedded with the VGG16 architecture. This self-attention module enables to learn the potentially distinguishing image features for better differentiability among gesture categories. After that, a weighted ensemble model is introduced, which uses the complementary information contributed by the base model to magnify the overall network performance. A detailed experimental investigation of the individual pre-trained models and the weighted ensemble of these models is conducted to validate the efficacy of the proposed network. Moreover, this work shows that the proposed model can also be employed for weakly supervised object segmentation. Additionally, the performance of the suggested methodology is evaluated on publicly available two complex hand gesture datasets and obtains an accuracy of 99.76% and 95.10%, which relatively outperforms other state-of-the-art approaches.

关键词： CNN sign language recognition system Deep learning Ensemble learning Human computer interaction

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Gesture recognition from RGB images using convolutional neural network-attention based system

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第24期34卷 e7230-e7230页

作者： Barbhuiya, Abul Abbas Karsh, Ram Kumar Jain, Rahul Natl Inst Technol Silchar Dept ECE Silchar 788010 India

Hand gestures can be categorized based on their applications as conversational, manipulative, controlling, and communicative gestures. Hand gesture recognition is an aspect of human action recognition, which plays a notable role in communication among the deaf and dumb community. Accurate recognition and classification of hand gestures with similar postures is still a complex problem. The main objective of this work is to employ deep learning based convolutional neural networks for human action recognition. This article introduces a convolutional neural network based on VGG16 architecture with an attention approach to mitigate the issue. Incorporating an attention-based module with the VGG16 architecture is the prime reason for potentially learning distinguishing image features by the network. Besides, the proposed robust system collectively recognizes and classifies all the available 36 hand gesture classes of the Massey database, where most previous work selected only a few distinguishable gestures. Experimental results show that the average recognition accuracy for characters with similar hand postures like "m" and "n" is better than the state-of-the-art networks. Additionally, the proposed method attains about 3% higher recognition accuracy than the state-of-the-art networks for all gesture classes. The efficacy of the proposed architecture has also been validated via precision, recall, and F-score.

关键词： CNN deep learning hand gesture human computer interaction sign language recognition system

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DCA-based unimodal feature-level fusion of orthogonal moments for Indian sign language dataset

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IET COMPUTER VISION 2018年第5期12卷 570-577页

作者： Joshi, Garima Vig, Renu Singh, Sukhwinder Panjab Univ UIET Elect & Commun Engn Dept Sect 25 Chandigarh India Panjab Univ UIET Comp Sci & Engn Dept Sect 25 Chandigarh India

sign language recognition system classifies signs made by hand gestures. An adequate number of features are required to represent the shape variations of sign language. As compared to individual feature set, a combination of features can be effective due to the fact that a particular feature set represents different shape information. A simple concatenation results in large feature vector size and increases the classification computational complexity. Discriminant correlation analysis (DCA)-based unimodal feature-level fusion has been applied on uniform as well as complex background Indian sign language datasets. DCA is a feature-level fusion technique that takes into account the class associations while combining the feature sets. It maximises the inter-class separability of two feature sets and also minimises the intra-class separability while performing the feature fusion. The objective of DCA-based unimodal feature fusion technique is to combine different feature sets into a single feature vector with more discriminative power. The performance of proposed framework is compared with individual orthogonal moment-based feature sets and canonical correlation analysis (CCA)-based feature fusion technique. Results show that in comparison to individual features and CCA-based fused features, DCA is an effective technique in terms of improved accuracy, reduced feature vector size and smaller classification time.

关键词： sign language recognition computational complexity correlation theory image fusion DCA-based unimodal feature-level fusion orthogonal moments Indian sign language dataset sign language recognition system hand gestures shape variations shape information unimodal feature fusion large feature vector size classification computational complexity discriminant correlation analysis unimodal feature-level fusion feature-level fusion technique intraclass separability single feature vector discriminative power canonical correlation analysis feature fusion technique CCA-based feature fusion technique

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An Accurate sign language Recognizer using Random Sphere Oracle 1

An Accurate Sign Language Recognizer using Random Sphere Ora...

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1st International Conference on Multimedia and Image Processing (ICMIP)

作者： Mustafa, Mumtaz Begum Lahsasna, Adel Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781467389402

In the present study, we propose an automatic sign language recognizer using an ensemble method called Random Sphere Oracle (RSO). The aim of using this method is to improve the accuracy of the existing sign language recognition (SLR) systems by using an efficient classifier to classify the sign language gestures into their respective classes. The design of the classifier composes of three steps. The first step aims to select the base classifier for the ensemble method, and this can be achieved by conducting a comparison between the classification accuracy of five benchmark classifiers, namely, Linear Discriminant Analysis (LDA), Classification and Regression Tree (CART), Naive Bayes (NB), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). The best classifier is used in the second step as a base classifier to generate a set of 50 classifiers using RSO. The last step' aim is to select among the 50 classifiers created in the second step, a subset of classifiers with the best performance. For this purpose, we apply a multi-objective genetic algorithm called controlled elitism NSGA-II to search for the best subset of classifiers that maximize two objectives: accuracy and diversity. All the classifiers are applied on a benchmark sign language data set and the results show that the proposed method has successfully improved the accuracy of the SLR system that uses the best single classifier.

关键词： ensemble methods sign language recognition system Genetic algorithms classification methods

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