Sign language recognition

Pahlevanzadeh, M.; Vafadoost, M.; Shahnazi, M.

doi:10.1109/isspa.2007.4555448

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Hong [3], Modler [17], Vadafar [18], Elmezain [19] Pizzolato [20], Bauer [21], Wang [22], Brasher [23] and Pahlevanzadeh [24] (just to mention a few), tackled the problem of dynamic gesture recognition. Hong proposed a system based on finite-state machines (FSMs).…”

Section: Related Workmentioning

confidence: 99%

An evaluation of real-time requirements for automatic sign language recognition using ANNs and HMMs - The LIBRAS use case

Pizzolato

Anjo

Feuerstack

2013

JIS

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Sign languages are the natural way Deafs use to communicate with other people. They have their own formal semantic definitions and syntactic rules and are composed by a large set of gestures involving hands and head. Automatic recognition of sign languages (ARSL) tries to recognize the signs and translate them into a written language. ARSL is a challenging task as it involves background segmentation, hands and head posture modeling, recognition and tracking, temporal analysis and syntactic and semantic interpretation. Moreover, when real-time requirements are considered, this task becomes even more challenging. In this paper, we present a study of real time requirements of automatic sign language recognition of small sets of static and dynamic gestures of the Brazilian Sign Language (LIBRAS). For the task of static gesture recognition, we implemented a system that is able to work on small sub-sets of the alphabet - like A,E,I,O,U and B,C,F,L,V - reaching very high recognition rates. For the task of dynamic gesture recognition, we tested our system over a small set of LIBRAS words and collected the execution times. The aim was to gather knowledge regarding execution time of all the recognition processes (like segmentation, analysis and recognition itself) to evaluate the feasibility of building a real-time system to recognize small sets of both static and dynamic gestures. Our findings indicate that the bottleneck of our current architecture is the recognition phase.

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Section: Related Workmentioning

confidence: 99%

An evaluation of real-time requirements for automatic sign language recognition using ANNs and HMMs - The LIBRAS use case

Pizzolato

Anjo

Feuerstack

2013

JIS

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“…Coogan and Sutherland [17] used a similar principle when they created a hierarchical decision tree, the leaf nodes of which contained the exemplar of a hand shape class, defined by fuzzy k-means clustering of the Eigenvalues resulting from performing PCA on the artificially constructed training images. Using gloved data to give good segmentation of the hands allowed Pahlevanzadeh et al to use a generic cosine detector to describe basic hand shapes [84] though the system is unlikely to be tractable. Fillbrandt et al used 2D appearance models to infer 3D posture and shape of the hands [31].…”

Section: Hand Shapementioning

confidence: 99%

Sign Language Recognition

Cooper

Holt

Bowden

2011

Visual Analysis of Humans

181

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This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

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“…Such approach can be used on the implementation of a recognition technique with satisfactory results [13]. The recognition algorithm has two phases, one dealing with hand configuration and other responsible for hand movement analysis.…”

Section: A) Capturing and Coding Sign Languagementioning

confidence: 99%

A model to support sign language content development for digital television

Brito

Pereira²

2009

2009 IEEE International Workshop on Multimedia Signal Processing

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This paper proposes a model to support sign language content development and deployment in digital television scenarios. The model objective is to overcome traditional video limitations on the production of signed content and improve accessibility on digital television. Previous work on sign and gesture recognition, sign notation systems and avatar animation are reviewed. Sign communication requirements are investigated and adequate algorithms are identified to enable sign language processing and transmission. An application interface is proposed in order to access the model functionalities. The final discussion lists the benefits and limitations of the proposed model.

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Sign language recognition

Cited by 6 publications

References 5 publications

An evaluation of real-time requirements for automatic sign language recognition using ANNs and HMMs - The LIBRAS use case

An evaluation of real-time requirements for automatic sign language recognition using ANNs and HMMs - The LIBRAS use case

Sign Language Recognition

A model to support sign language content development for digital television

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