Top Research Papers on Sign Language Recognition

A taxonomy to categorize the proposed models for isolated and continuous sign language recognition is presented, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field.

Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment, and the model yielded 65% accuracy.

Robust modeling of static signs in the context of sign language recognition using deep learning-based convolutional neural networks (CNN) and demonstrates its effectiveness over the earlier works in which only a few hand signs are considered for recognition.

This paper introduces a robust pose normalization scheme which takes the signing space in consideration and processes the hand poses in a separate local coordinate system, independent on the body pose, and demonstrates the significant impact of this normalization on the accuracy of the proposed system.

A novel skeleton Aware Multimodal SLR framework (SAM-SLR) is proposed and a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and a novel Separable SpatialTemporal Convolution network (SSTCN) to exploit skeleton features to improve the recognition rate.

This work revisits the overfitting problem in recent CTC-based CSLR works and proposes a Visual Alignment Constraint (VAC) to enhance the feature extractor with more alignment supervision and proposes two metrics to evaluate the contributions of the feature Extractor and the alignment model, which provide evidence for the over fitting problem.

A detailed review of all the works in vision-based CSLR is presented, based on the methods they have followed, followed by a brief on sensor-based systems and benchmark databases.

This study proposed an efficient deep convolutional neural networks approach for hand gesture recognition that employed transfer learning to beat the scarcity of a large labeled hand gesture dataset.

A novel method is proposed that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them, and outperforms previous state-of-the-art methods significantly.

Analysis of studies that use wearable sensor-based systems to classify sign language gestures finds trends, best practices, and common challenges could aid in the development of user-centred and robust wearable sensors for sign language recognition.

A Self-Mutual Knowledge Distillation (SMKD) method is proposed, which enforces the visual and contextual modules to focus on short-term and long-term information and enhances the discriminative power of both modules simultaneously.

Hand signs are an effective form of human-to-human communication that has a number of possible applications. Being a natural means of interaction, they are commonly used for communication purposes by speech impaired people worldwide. In fact, about one percent of the Indian population belongs to this category. This is the key reason why it would have a huge beneficial effect on these individuals to incorporate a framework that would understand Indian Sign Language. In this paper, we present a technique that uses the Bag of Visual Words model (BOVW) to recognize Indian sign language alphabets (...

Evaluation results of the proposed model indicate that the model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition.

Long-Short Term Memory Recurrent Neural Network with k-Nearest-Neighbour method is adopted as the classification method is based on handling of sequences of input, and revealed that the recognition rate for 26 ASL alphabets yields an average of 99.44% accuracy rate.

The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses, by implementing the most recent deep neural network methods in this field.

This work proposes a spatial-temporal multi-cue (STMC) network, which aims to solve the vision-based sequence learning problem in continuous sign language recognition and designs a joint optimization strategy to achieve the end-to-end sequence learning of the STMC network.

The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem.

Abstract Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved ...

A spatial-temporal multi-cue (STMC) network is proposed to solve the vision-based sequence learning problem in video-based sign language understanding and achieves new state-of-the-art performance on all three benchmarks.

This work proposes a novel architecture with cross modality augmentation for continuous sign language recognition that can be easily extended to other existing CTC based continuous SLR architectures and validates the effectiveness of the proposed method on two continuousSLR benchmarks.