@inproceedings{dimou:12018:sign-lang:lrec,
  author    = {Dimou, Athanasia-Lida and Pitsikalis, Vassilis and Goulas, Theodoros and Theodorakis, Stavros and Karioris, Panagiotis and Pissaris, Michalis and Fotinea, Stavroula-Evita and Efthimiou, Eleni and Maragos, Petros},
  title     = {A {GSL} continuous phrase corpus: Design and acquisition},
  pages     = {23--26},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12018.html},
  abstract  = {The corpus presented in this article is composed of a limited number of Greek Sign Language (GSL) sentences and was created in order to provide additional data to the already obtained corpus during the first year of the Dicta-Sign project (Matthes et al., 2010). More specifically this corpus intended to serve as the ground upon which a significant part of the recognition process would be tested and evaluated, more precisely, the continuous sign language recognition algorithms developed in the project.
\par
Given the targeted nature of this corpus we present here the constraints as well as the procedure followed in order to obtain it.
\par
The procedure followed for the creation of this corpus, consists of its linguistic design and validation, the studio and hardware acquisition configuration, the implementation and supervision of the acquisition itself and the post-processing and annotation of the obtained data in order to release the set of usable annotated resources. The specific GSL phrase corpus forms the basis for machine learning and training to serve experimentation in the domain of continuous sign language processing and recognition.}
}

@inproceedings{efthimiou:12025:sign-lang:lrec,
  author    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Glauert, John and Bowden, Richard and Braffort, Annelies and Collet, Christophe and Maragos, Petros and Lefebvre-Albaret, Fran{\c c}ois},
  title     = {Sign Language technologies and resources of the {Dicta-Sign} project},
  pages     = {37--44},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12025.html},
  abstract  = {Here we present the outcomes of Dicta-Sign FP7-ICT project. Dicta-Sign researched ways to enable communication between Deaf individuals through the development of human-computer interfaces (HCI) for Deaf users, by means of Sign Language. It has researched and developed recognition and synthesis engines for sign languages (SLs) that have brought sign recognition and generation technologies significantly closer to authentic signing. In this context, Dicta-Sign has developed several technologies demonstrated via a sign language aware Web 2.0, combining work from the fields of sign language recognition, sign language animation via avatars and sign language resources and language models development, with the goal of allowing Deaf users to make, edit, and review avatar-based sign language contributions online, similar to the way people nowadays make text-based contributions on the Web.}
}

@inproceedings{efthimiou:10027:sign-lang:lrec,
  author    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Glauert, John and Bowden, Richard and Braffort, Annelies and Collet, Christophe and Maragos, Petros and Goudenove, Fran{\c c}ois},
  title     = {{DICTA-SIGN}: Sign Language Recognition, Generation and Modelling with application in Deaf Communication},
  pages     = {80--83},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10027.html},
  abstract  = {Here we present the components and objectives of the EU funded project DICTA-SIGN. Dicta-Sign (http://www.dictasign.eu) is a three-year research project that involves the Institute for Language and Speech Processing, the University of Hamburg, the University of East Anglia, the University of Surrey, LIMSI/CNRS, the Universit{\'e} Paul Sabatier, the National Technical University of Athens, and WebSourd. It aims to improve the state of web-based communication for Deaf people by allowing the use of sign language in various human-computer interaction scenarios. It researches and develops recognition and synthesis engines for signed languages, aiming at a level of detail necessary for recognizing and generating authentic signing. In this context, Dicta-Sign aims at developing several technologies demonstrated via a sign language-aware Web 2.0. 
\par
Dicta-Sign supports four European sign languages: Greek. British, German, and French Sign Language and differs from previous work in that it aims to integrate tightly recognition, animation, and machine translation. All these components are informed by appropriate linguistic models from the ground up, including phonology, grammar, and non-manual features. 
\par
Expected outputs of the project include:\begin{itemize}\item A parallel multi-lingual corpus for four national sign languages - German, British, French and Greek (DGS, BSL, LSF and GSL respectively),\item A substantial multilingual dictionary of at least 1000 signs for each represented sign language,\item A continuous sign language recognition system that achieves significant improvement in terms of coverage and accuracy of sign recognition in comparison with current technology; furthermore this system will research the novel directions of multimodal sign fusion and signer adaptation,\item A language generation and synthesis component, covering in detail the role of manual, non-manual and placement within signing space,\item Annotation tools which incorporate these technologies providing access to the corpus and whose long term utility can be judged by the up-take by other sign language researchers,\item Three bidirectional integrated prototype systems which show the utility of the system components beyond the annotation tools application,\item A showcase demonstrator which exhibits how integration of the different components can support user communication needs.\end{itemize}}
}

@inproceedings{pitsikalis:10049:sign-lang:lrec,
  author    = {Pitsikalis, Vassilis and Theodorakis, Stavros and Maragos, Petros},
  title     = {Data-Driven Sub-Units, Modeling Structure of Multiple Cues for Continuous Sign Language Recognition},
  pages     = {196--203},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10049.html},
  abstract  = {We investigate the automatic phonetic modeling of sign language based on phonetic sub-units, which are data driven and without any prior phonetic information. Visual processing is based on a probabilistic skin color model and a framewise geodesic active contour segmentation; occlusions are handled by a forward-backward prediction component leading finally to simple and effective region-based visual features. For sign-language modeling we propose a modeling structure for data-driven sub-unit construction. This utilizes the cue that is considered crucial to segment the signal into parts; at the same time we also classify the segments by implicitly assigning labels of Dynamic or Static type. This segmentation and classification step disentangles Dynamic from Static parts and allows us to employ for each type of segment the appropriate cue, modeling and clustering approach. The constructed Dynamic segments are exploited at the model level via hidden Markov models (HMMs). The Static segments are exploited via k-means clustering. Each Dynamic or Static part, exploits the appropriate cue related to the movement. We propose that the movement cues are normalized in order to be translation and scale invariant. We apply the proposed modeling for further combination of the movement trajectory individual cues. The proposed approaches are evaluated in recognition experiments conducted on the continuous sign language corpus of Boston University (BU-400) showing promising preliminary results.}
}

@inproceedings{papadimitriou:70026:sltat:lrec,
  author    = {Papadimitriou, Katerina and Potamianos, Gerasimos and Sapountzaki, Galini and Goulas, Theodoros and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Maragos, Petros},
  title     = {{Greek} {Sign} {Language} Recognition for the {SL-ReDu} Learning Platform},
  pages     = {79--84},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee},
  booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {24},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-82-5},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/2022.sltat-1.12.html},
  abstract  = {There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners' SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign Language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively.}
}