AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software

• URL: http://arxiv.org/abs/2411.12865v2
• Date: Sat, 23 Nov 2024 12:37:54 GMT
• Title: AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software
• Authors: Nigar Alishzade, Jamaladdin Hasanov,
• Abstract summary: The dataset was created within the framework of a vision-based AzSL translation project. AzSLD contains 30,000 videos, each carefully annotated with accurate sign labels and corresponding linguistic translations.
• Score: 0.0
• License:
• Abstract: Sign language processing technology development relies on extensive and reliable datasets, instructions, and ethical guidelines. We present a comprehensive Azerbaijani Sign Language Dataset (AzSLD) collected from diverse sign language users and linguistic parameters to facilitate advancements in sign recognition and translation systems and support the local sign language community. The dataset was created within the framework of a vision-based AzSL translation project. This study introduces the dataset as a summary of the fingerspelling alphabet and sentence- and word-level sign language datasets. The dataset was collected from signers of different ages, genders, and signing styles, with videos recorded from two camera angles to capture each sign in full detail. This approach ensures robust training and evaluation of gesture recognition models. AzSLD contains 30,000 videos, each carefully annotated with accurate sign labels and corresponding linguistic translations. The dataset is accompanied by technical documentation and source code to facilitate its use in training and testing. This dataset offers a valuable resource of labeled data for researchers and developers working on sign language recognition, translation, or synthesis. Ethical guidelines were strictly followed throughout the project, with all participants providing informed consent for collecting, publishing, and using the data.

Related papers

• Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues [56.038123093599815]
  Our objective is to translate continuous sign language into spoken language text. We incorporate additional contextual cues together with the signing video. We show that our contextual approach significantly enhances the quality of the translations.
  arXiv  Detail & Related papers  (2025-01-16T18:59:03Z)
• Signs as Tokens: An Autoregressive Multilingual Sign Language Generator [55.94334001112357]
  We introduce a multilingual sign language model, Signs as Tokens (SOKE), which can generate 3D sign avatars autoregressively from text inputs. To align sign language with the LM, we develop a decoupled tokenizer that discretizes continuous signs into token sequences representing various body parts. These sign tokens are integrated into the raw text vocabulary of the LM, allowing for supervised fine-tuning on sign language datasets.
  arXiv  Detail & Related papers  (2024-11-26T18:28:09Z)
• Open the Data! Chuvash Datasets [50.59120569845975]
  We introduce four comprehensive datasets for the Chuvash language. These datasets include a monolingual dataset, a parallel dataset with Russian, a parallel dataset with English, and an audio dataset.
  arXiv  Detail & Related papers  (2024-05-31T07:51:19Z)
• ISLTranslate: Dataset for Translating Indian Sign Language [4.836352379142503]
  This paper introduces ISLTranslate, a translation dataset for continuous Indian Sign Language (ISL) consisting of 31k ISL-English sentence/phrase pairs. To the best of our knowledge, it is the largest translation dataset for continuous Indian Sign Language.
  arXiv  Detail & Related papers  (2023-07-11T17:06:52Z)
• Slovo: Russian Sign Language Dataset [83.93252084624997]
  This paper presents the Russian Sign Language (RSL) video dataset Slovo, produced using crowdsourcing platforms. The dataset contains 20,000 FullHD recordings, divided into 1,000 classes of isolated RSL gestures received by 194 signers.
  arXiv  Detail & Related papers  (2023-05-23T21:00:42Z)
• ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition [6.296362537531586]
  Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition dataset. We propose that this dataset be used for sign language dictionary retrieval for American Sign Language (ASL), where a user demonstrates a sign to their webcam to retrieve matching signs from a dictionary.
  arXiv  Detail & Related papers  (2023-04-12T15:52:53Z)
• On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study [5.849513679510834]
  We analyze the HamNoSys labels provided by various maintainers of open sign language corpora in five sign languages. Our findings provide valuable insights into the limitations of the current labeling methods.
  arXiv  Detail & Related papers  (2023-01-19T11:11:57Z)
• LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation [52.87578398308052]
  Sign language translation (SLT) is an active field of study that encompasses human-computer interaction, computer vision, natural language processing and machine learning. This paper presents the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the CN Sordos YouTube channel with labels and keypoints annotations for each signer.
  arXiv  Detail & Related papers  (2022-11-14T14:46:44Z)
• ASL-Homework-RGBD Dataset: An annotated dataset of 45 fluent and non-fluent signers performing American Sign Language homeworks [32.3809065803553]
  This dataset contains videos of fluent and non-fluent signers using American Sign Language (ASL) A total of 45 fluent and non-fluent participants were asked to perform signing homework assignments. The data is annotated to identify several aspects of signing including grammatical features and non-manual markers.
  arXiv  Detail & Related papers  (2022-07-08T17:18:49Z)
• Watch, read and lookup: learning to spot signs from multiple supervisors [99.50956498009094]
  Given 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. We train a model using multiple types of available supervision by: (1) watching existing sparsely labelled footage; (2) reading associated subtitles which provide additional weak-supervision; and (3) looking up words in visual sign language dictionaries. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning.
  arXiv  Detail & Related papers  (2020-10-08T14:12:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.