115,331 research outputs found
LSA64: An Argentinian Sign Language Dataset
Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population.
Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language.
This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI
LSA64: An Argentinian Sign Language Dataset
Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population.
Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language.
This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI
Adversarial Training for Multi-Channel Sign Language Production
Sign Languages are rich multi-channel languages, requiring articulation of
both manual (hands) and non-manual (face and body) features in a precise,
intricate manner. Sign Language Production (SLP), the automatic translation
from spoken to sign languages, must embody this full sign morphology to be
truly understandable by the Deaf community. Previous work has mainly focused on
manual feature production, with an under-articulated output caused by
regression to the mean.
In this paper, we propose an Adversarial Multi-Channel approach to SLP. We
frame sign production as a minimax game between a transformer-based Generator
and a conditional Discriminator. Our adversarial discriminator evaluates the
realism of sign production conditioned on the source text, pushing the
generator towards a realistic and articulate output. Additionally, we fully
encapsulate sign articulators with the inclusion of non-manual features,
producing facial features and mouthing patterns.
We evaluate on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T)
dataset, and report state-of-the art SLP back-translation performance for
manual production. We set new benchmarks for the production of multi-channel
sign to underpin future research into realistic SLP
LSA64: An Argentinian Sign Language Dataset
Automatic sign language recognition is a research area that encompasses
human-computer interaction, computer vision and machine learning. Robust
automatic recognition of sign language could assist in the translation process
and the integration of hearing-impaired people, as well as the teaching of sign
language to the hearing population. Sign languages differ significantly in
different countries and even regions, and their syntax and semantics are
different as well from those of written languages. While the techniques for
automatic sign language recognition are mostly the same for different
languages, training a recognition system for a new language requires having an
entire dataset for that language. This paper presents a dataset of 64 signs
from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains
3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first
step towards building a comprehensive research-level dataset of Argentinian
signs, specifically tailored to sign language recognition or other machine
learning tasks. The subjects that performed the signs wore colored gloves to
ease the hand tracking and segmentation steps, allowing experiments on the
dataset to focus specifically on the recognition of signs. We also present a
pre-processed version of the dataset, from which we computed statistics of
movement, position and handshape of the signs.Comment: Published in CACIC XXI
LSA64: An Argentinian Sign Language Dataset
Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population.
Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language.
This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people.
This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people.
This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of
human-computer interaction and machine learning. On the one hand, it poses a
complex challenge that requires the intervention of various knowledge areas,
such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people.
This paper offers two main contributions: first, the creation of a database
of handshapes for the Argentinian Sign Language (LSA), which is a topic that
has barely been discussed so far. Secondly, a technique for image processing,
descriptor extraction and subsequent handshape classification using a
supervised adaptation of self-organizing maps that is called ProbSom. This
technique is compared to others in the state of the art, such as Support Vector
Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA handshapes, and
is a first step towards building a comprehensive database of Argentinian signs.
The ProbSom-based neural classifier, using the proposed descriptor, achieved an
accuracy rate above 90%
Assistive translation technology for deaf people: translating into and animating Irish sign language
Machine Translation (MT) for sign languages (SLs) can facilitate communication between Deaf and hearing people by translating information into the native and preferred language of the individuals. In this paper, we discuss automatic translation from English to Irish SL (ISL) in the domain of airport information. We describe our data collection processes and the architecture of the MaTrEx system used for our translation work. This is followed by an outline of the additional animation phase that transforms the translated output into animated ISL. Through a set of experiments, evaluated both automatically and
manually, we show that MT has the potential to assist Deaf people by providing information in their first language
Joining hands: developing a sign language machine translation system with and for the deaf community
This paper discusses the development of an automatic machine translation (MT) system for translating spoken language text into signed languages (SLs). The motivation for our work is the improvement of accessibility to airport information announcements for D/deaf and hard of hearing people. This paper demonstrates the involvement of Deaf colleagues and members of the D/deaf community in Ireland in three areas of our research: the choice of a domain for automatic translation that has a practical use for the D/deaf community; the human translation of English text into Irish Sign Language (ISL) as well as advice on ISL grammar and linguistics; and the importance of native ISL signers as manual evaluators of our translated output
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