Sign Transition Modeling and a Scalable Solution to Continuous Sign Language Recognition for Real-World Applications | Semantic Scholar (2025)

MyoSign is presented, a deep learning based system that enables end-to-end American Sign Language (ASL) recognition at both word and sentence levels and uses a lightweight wearable device which can provide inertial and electromyography signals to non-intrusively capture signs.

A novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation.

It appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis in sign language recognition, and a general framework for researchers is proposed.

A modified long short-term memory (LSTM) model for continuous sequences of gestures or continuous SLR that recognizes a sequence of connected gestures based on splitting of continuous signs into sub-units and modeling them with neural networks is proposed.

The result shows the first SmartWatch-based American sign language (ASL) recognition system, which is more comfortable, portable and user-friendly and offers accessibility anytime, anywhere, has a good adaptation and performs real time ASL translation.

SignRing is proposed, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses.

A multimodal dynamic sign language recognition method based on a deep 3-dimensional residual ConvNet and bi-directional LSTM networks, which is named as BLSTM-3D residual network (B3D ResNet), which can obtain state-of-the-art recognition accuracy.

The hierarchical structure of sign language signal can be encoded by the boundary-adaptive encoder (BAE) in the proposed method and the window attention model based on location is utilized in the decoding phase, which can generate more effective weight coefficients.

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This work presents a large-vocabulary ASLR system that is able to recognize sentences in continuous sign language and uses features extracted from standard single-view video cameras without using additional equipment.

Transition-movement models (TMMs) are proposed to handle transition parts between two adjacent signs in large-vocabulary continuous SLR and are viewed as candidates of the Viterbi search algorithm for recognizing continuous sign language.

This work presents an approach to large vocabulary, continuous Chinese sign language (CSL) recognition that uses phonemes instead of whole signs as the basic units, and facilitates the CSL recognition when the finger-alphabet is blended with gestures.

This work presents their approach where the system developed is able to recognize sentences of continuous sign language independent of the speaker, and focuses on feature and model combination techniques applied in ASR, and the usage of pronunciation and language models (LM) in sign language.

A novel approach to ASL recognition that aspires to being a solution to the scalability problems, based on parallel HMMs (PaHMMs), which model the parallel processes independently and can be trained independently, and do not require consideration of the different combinations at training time.

A large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a data glove using hidden Markov models for 51 fundamental postures, 6 orientations, and 8 motion primitives.

A novel approach for handling OOV words within a single-stage recognition framework that achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words.

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