BBC-Oxford British Sign Language Dataset

Albanie, Samuel; Varol, Gül; Momeni, Liliane; Bull, Hannah; Afouras, Triantafyllos; Chowdhury, Himel; Fox, Neil I.; Woll, Bencie; Cooper, Robert; McParland, Andrew; Zisserman, Andrew

doi:10.48550/arxiv.2111.03635

Cited by 5 publications

(31 citation statements)

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“…BOBSL [4] is a dataset similar to BSL-1K; however, unlike BSL-1K, BOBSL is publicly available. The dataset consists of 1,400 hours of BSL-interpreted BBC broadcast footage accompanied by written English subtitles.…”

Section: Results On the Bobsl Datasetmentioning

confidence: 99%

“…Please watch our video in the project webpage 4 to see qualitative results of our model in action. We illustrate the sign spotting task, as well as the specific applications considered in the main paper: sign variant identification, densification of annotations, and "faux amis" identification between languages.…”

Section: A Qualitative Resultsmentioning

confidence: 99%

“…We show qualitatively that the learned embeddings can be used to (5) automatically mine new signing examples, and (6) discover "faux amis" (false friends) between sign languages. In addition, we extend these contributions with (7) the demonstration that our framework can be effectively deployed to obtain large numbers of sign examples, enabling state-of-the-art performance to be reached on the BSL-1K sign recognition benchmark [5], and on the recently released BOBSL dataset [4].…”

Section: Similarity Scorementioning

confidence: 98%

“…We show through experiments in Sec. 4, that directly training a sign spotter for continuous signing on dictionary examples, obtained from an internet-sourced sign language dictionary, does indeed perform poorly.…”

Section: Similarity Scorementioning

confidence: 99%

“…1 and described in more detail below. We further include experiments on a new dataset, BOBSL [4], which we describe in Sec. 4.7 together with results.…”

Section: Datasetsmentioning

confidence: 99%

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Scaling up sign spotting through sign language dictionaries

Varol,

Momeni,

Albanie

et al. 2022

Preprint

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The focus of this work is sign spottinggiven a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) watching existing footage which is sparsely labelled using mouthing cues;(2) reading associated subtitles (readily available translations of the signed content) which provide additional weak-supervision; (3) looking up words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, BslDict, to facilitate study of this task. The dataset, models and code are available at our project page.

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Section: Results On the Bobsl Datasetmentioning

confidence: 99%

Section: A Qualitative Resultsmentioning

confidence: 99%

Section: Similarity Scorementioning

confidence: 98%

Section: Similarity Scorementioning

confidence: 99%

“…1 and described in more detail below. We further include experiments on a new dataset, BOBSL [4], which we describe in Sec. 4.7 together with results.…”

Section: Datasetsmentioning

confidence: 99%

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Scaling up sign spotting through sign language dictionaries

Varol,

Momeni,

Albanie

et al. 2022

Preprint

Self Cite

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Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges

Tao,

Zhao,

Liu

et al. 2024

IEEE Access

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The Deaf are a large social group in society. Their unique way of communicating through sign language is often confined within their community due to limited understanding by individuals outside of this demographic. This is where sign language recognition (SLR) comes in to help normal people understand the meaning of sign language. In recent years, new methods of sign language recognition have been developed and achieved good results, so it is necessary to make a summary. This review mainly focuses on the introduction of sign language recognition techniques based on algorithms especially in recent years, including the recognition models based on traditional methods and deep learning approaches, sign language datasets, challenges and future directions in SLR. To make the method structure clearer, this article explains and compares the basic principles of different methods from the perspectives of feature extraction and temporal modelling. We hope that this review will provide some reference and help for future research in sign language recognition.

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The Serbian Sign Language Alphabet: A Unique Authentic Dataset of Letter Sign Gestures

Radaković,

Marjanović,

Ristić

et al. 2024

Mathematics

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Language barriers and the communication difficulties of individuals with developmental disabilities are two major causes of communication problems that societies worldwide encounter. A particularly challenging group is hearing-impaired people who have difficulties with communication, reading, writing, learning, and social interactions, which have a substantial impact on their quality of life. This article focuses on detailing a Serbian Sign Language alphabet database and the method for creating it in order to provide a foundation for answering the various societal challenges of persons who use the Serbian language. In front of a computer camera, 41 people performed Serbian Sign Language sign movements that replicated the Serbian alphabet for this study’s aims. Hand and body key points were identified using the recorded video clips, and the numerical values of the identified key points were then stored in a database for further processing. In total, 8.346 video clips of people making recognized hand gestures were gathered, processed, classed, and archived. This paper provides a thorough technique that may be applied to comparable tasks and details the process of constructing a dataset based on Serbian Sign Language alphabet signs. This dataset was created using custom-made Python 3.11 software. Data regarding dynamic video clips that capture entire subject movement were incorporated into this dataset to fill in the gaps in other similar efforts based on static photographs. Thus, the purpose of this investigation is to employ innovative technology to support the community of hearing-impaired people in areas such as general inclusion, education, communication, and empowerment.

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BBC-Oxford British Sign Language Dataset

Cited by 5 publications

References 0 publications

Scaling up sign spotting through sign language dictionaries

Scaling up sign spotting through sign language dictionaries

Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges

The Serbian Sign Language Alphabet: A Unique Authentic Dataset of Letter Sign Gestures

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