Sign Language Gesture Recognition Using HMM

Parcheta, Zuzanna; Martínez-Hinarejos, Carlos D.

doi:10.1007/978-3-319-58838-4_46

Cited by 25 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this work, Convolutional Networks are used to classify gestures from the Spanish sign language. Some improvements in recognition accuracy are obtained with respect to results from our previous studies [8,9].…”

Section: Related Worksupporting

confidence: 50%

“…In [18], the Microsoft Kinect is used to obtain features to identify 8 different signs using weighted Dynamic Time Warping (DTW). The latter approach is mentioned in [8], where a combination of k-Nearest Neighbour classifiers and DTW allows the recognition of 91 signs extracted by using the Leap Motion sensor. This work was continued in [9] where different typologies of Continuous Density Hidden Markov Models were applied.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental data is the isolated gestures subset from the "Spanish sign language db" 3 , which is described in [8]. All gestures in this database are dynamic.…”

Section: Experimental Setup 31 Data Preparationmentioning

confidence: 99%

“…In this work, we explore Convolutional Neural Networks (CNN) to perform gesture classification for isolated words coming from a sign language database generated using the Leap Motion 1 sensor. Previous work [8,9] demonstrated that it is possible to classify this dynamic gesture database using Hidden Markov Models with a 10.6% error rate. In our current work, this score is significantly improved to an error rate of 8.6%.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sign Language Gesture Classification using Neural Networks

Parcheta¹,

Martínez-Hinarejos²

2018

IberSPEECH 2018

Self Cite

View full text Add to dashboard Cite

Recent studies have demonstrated the power of neural networks for different fields of artificial intelligence. In most fields, such as machine translation or speech recognition, neural networks outperform previously used methods (Hidden Markov Models with Gaussian Mixtures, Statistical Machine Translation, etc.). In this paper, the efficiency of the LeNet convolutional neural network for isolated word sign language recognition is demonstrated. As a preprocessing step, we apply several techniques to obtain the same dimension for the input that contains gesture information. The performance of these preprocessing techniques on a Spanish Sign Language dataset is evaluated. These approaches outperform previously obtained results based on Hidden Markov Models.

show abstract

Section: Related Worksupporting

confidence: 50%

Section: Related Workmentioning

confidence: 99%

“…The experimental data is the isolated gestures subset from the "Spanish sign language db" 3 , which is described in [8]. All gestures in this database are dynamic.…”

Section: Experimental Setup 31 Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sign Language Gesture Classification using Neural Networks

Parcheta¹,

Martínez-Hinarejos²

2018

IberSPEECH 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data augmentation [2] transforms the data in case of images by enlarging them, trimming them, changing the angle, altering the background and changing the illumination. Other possibilities are to artificially create hand gestures [3] or to record hand gestures with a device like Leap Motion which instead of acquiring images, captures the 3 dimensional movement of the hands and each finger [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Experimental Framework Design for Sign Language Automatic Recognition

Santiago¹,

Benderitter²,

Garcı́a-Mateo³

2018

IberSPEECH 2018

View full text Add to dashboard Cite

Automatic sign language recognition (ASLR) is quite a complex task, not only for the intrinsic difficulty of automatic video information retrieval, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (SSL) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, the problem of how to design an experimental framework for machine-learning-based ASLR is addressed. In our review of existing datasets, our main conclusion is that there is a need for high-quality data. We therefore propose some guidelines on how to conduct the acquisition and annotation of an SSL dataset. These guidelines were developed after conducting some preliminary ASLR experiments with small and limited subsets of existing datasets.

show abstract

Virtual Reality Tool for Learning Sign Language in Spanish

Viloria

Llerena

Lezama

2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Language is the means of human access to the world. Languages have the virtue of opening up alternative ways of thinking and understanding the place people inhabit, relating to it, expanding it and modifying it. As a possibility of communication, languages open up opportunities to relate to other people, to get closer to them and to develop a broader understanding of the social and the human elements [1]. This research presents a visual tool designed to allow the learning of multiple words that are part of Spanish Sign Language (SSL) through an anthropomorphic model that is completely manipulable and programmable.

show abstract

Sign Language Gesture Recognition Using HMM

Cited by 25 publications

References 3 publications

Sign Language Gesture Classification using Neural Networks

Sign Language Gesture Classification using Neural Networks

Experimental Framework Design for Sign Language Automatic Recognition

Virtual Reality Tool for Learning Sign Language in Spanish

Contact Info

Product

Resources

About