Sign Language Recognition and Translation: A Multidisciplined Approach From the Field of Artificial Intelligence

Parton, Becky Sue

doi:10.1093/deafed/enj003

Cited by 69 publications

(23 citation statements)

References 13 publications

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“…1). In this paper, signs are words in the predefined vocabulary and fingerspellings are a combination of continuous alphabets e.g., names of people, etc [2,3]. An alphabet is a particular hand configuration [2,3].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings

Yang

Lee

2010

Pattern Recognition

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Section: Introductionmentioning

confidence: 99%

Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings

Yang

Lee

2010

Pattern Recognition

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“…In this paper, signs are words in the predefined vocabulary and fingerspellings are a combination of continuous alphabets e.g., names of people, etc [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Robust Sign Language Recognition with Hierarchical Conditional Random Fields

Yang

Lee

2010

2010 20th International Conference on Pattern Recognition

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Sign language spotting is the task of detection and recognition of signs (words in the predefined vocabulary) and fingerspellings (a combination of continuous alphabets that are not found in signs) in a signed utterance. The internal structures of signs and fingerspellings differ significantly. Therefore, it is difficult to spot signs and fingerspellings simultaneously. In this paper, a novel method for spotting signs and fingerspellings is proposed, which can distinguish signs, fingerspellings, and nonsign patterns. This is achieved through a hierarchical framework consisting of three steps; (1) Candidate segments of signs and fingerspellings are discriminated with a two-layer conditional random field (CRF). (2) Hand shapes of detected signs and fingerspellings are verified by BoostMap embeddings. (3) The motions of fingerspellings are verified in order to distinguish those which have similar hand shapes and differ only in hand trajectories. Experiments demonstrate that the proposed method can spot signs and fingerspellings from utterance data at rates of 83% and 78%, respectively.

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“…SLR systems have been rapidly developed in recent years for different sign languages including American [1-3], Chinese [4], Australian [5], Arabic [6,7], Indian [8], Spanish [9] and Japanese [10]. For more reviews on sign language and different approaches developed for SLR systems refer to [11,12].Due to its broad range of capabilities, Machine Vision (MV) is the major tool used in the development of SLR systems. An MV-based SLR system usually consists of three components: hand tracker, feature extractor and classifier.…”

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confidence: 99%

Trajectory-based recognition of dynamic Persian sign language using hidden Markov model

Azar

Seyedarabi²

2020

Computer Speech & Language

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Sign Language Recognition (SLR) is an important step in facilitating the communication among deaf people and the rest of society. Existing Persian sign language recognition systems are mainly restricted to static signs which are not very useful in everyday communications.In this study, a dynamic Persian sign language recognition system is presented. A collection of 1200 videos were captured from 12 individuals performing 20 dynamic signs with a simple white glove. The trajectory of the hands, along with hand shape information were extracted from each video using a simple region-growing technique. These time-varying trajectories were then modeled using Hidden Markov Model (HMM) with Gaussian probability density functions as observations. The performance of the system was evaluated in different experimental strategies. Signer-independent and signer-dependent experiments were performed on the proposed system and the average accuracy of 97.48% was obtained. The experimental results demonstrated that the performance of the system is independent of the subject and it can also perform excellently even with a limited number of training data. are manual gestures, while non-manual gestures like head movements (e.g. nodding), body movement (e.g. shrugging) and face expressions also play an important role in sign communications. Unfortunately, the use of sign language is usually restricted to the deaf community resulting in restricted communication of them with the rest of the world. Therefore, they are usually excluded from society and deprived of their rights to have equal educational and career opportunities. In order to address this problem, Sign Language Recognition (SLR) systems are developed to translate this language into speech or text. Developing efficient SLR systems can facilitate the communication of deaf people in society and remove the barriers for them.One of the basic issues regarding SLR is that there is not a universal sign language.Sign languages of different countries have their own grammar rules. SLR systems have been rapidly developed in recent years for different sign languages including American [1-3], Chinese [4], Australian [5], Arabic [6,7], Indian [8], Spanish [9] and Japanese [10]. For more reviews on sign language and different approaches developed for SLR systems refer to [11,12].Due to its broad range of capabilities, Machine Vision (MV) is the major tool used in the development of SLR systems. An MV-based SLR system usually consists of three components: hand tracker, feature extractor and classifier. Hand trackers job is to segment hand regions from the background of the input video frames. Some studies rely on data gloves to track the hand movements for hand tracking [6,[13][14][15][16]. Although these gloves are

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Sign Language Recognition and Translation: A Multidisciplined Approach From the Field of Artificial Intelligence

Cited by 69 publications

References 13 publications

Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings

Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings

Robust Sign Language Recognition with Hierarchical Conditional Random Fields

Trajectory-based recognition of dynamic Persian sign language using hidden Markov model

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