A Real-Time Large Vocabulary Recognition System for Chinese Sign Language

Wang, Chunli; Gao, Wen; Ma, Jiyong

doi:10.1007/3-540-47873-6_9

Cited by 12 publications

(3 citation statements)

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“…The studies on sign language recognition were not limited in a specific language, so a real-time system was developed to detect the continuous Chinese sign language (CSL) [13]. The system used a 3D tracker and two cybergloves for collecting data.…”

Section: Related Workmentioning

confidence: 99%

A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Kolivand

Joudaki

Sunar

et al. 2020

Neural Comput & Applic

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Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people with speech impairment to communication with normal people. This framework which is called ASLNN proposes a new hand posture recognition technique for the American sign language alphabet based on the neural network which works on the geometrical feature extraction of hands. A user's hand is captured by a threedimensional depth-based sensor camera; consequently, the hand is segmented according to the depth analysis features. The proposed system is called depth-based geometrical sign language recognition as named DGSLR. The DGSLR adopted in easier hand segmentation approach, which is further used in segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation compared to discrete cosine transform and moment invariant. The findings of the iterations demonstrate the combination of the extracted features resulted to improved accuracy rates. Then, an artificial neural network is used to drive desired outcomes. ASLNN is proficient to hand posture recognition and provides accuracy up to 96.78% which will be discussed on the additional paper of this authors in this journal. Keywords Sign language alphabet Á Hand posture recognition Á Depth-based geometrical sign language Á Geometrical features sign language This work is prepared in two separated papers due to the complexity of prepared materials and needs of much discussion on the obtained results. Both papers are submitted at the same time in Neural Computing and Applications.

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Section: Related Workmentioning

confidence: 99%

A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Kolivand

Joudaki

Sunar

et al. 2020

Neural Comput & Applic

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show abstract

“…Furthermore, the adaptive fuzzy expert system is applied for 22 signs with more than 95% results [5]. Chula et al (2002) discussed the major challenges of Sign Language recognition system. In this research, a real-time application developed for Chinese Sign Language using 5100 gestures.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A cyber glove and a 3D tracker is used to extract gestures based on geometrical analysis. The Gaussians and quick matching algorithms are introduced to improve the performance along with Hidden Markov Models (HMMs) [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Sign Language Recognition using Hybrid Neural Networks

Singh*¹,

Gupta²,

Singh³

2019

IJITEE

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Language has a prime role in communication between persons, in learning, in distribution of concepts and in preserving public contacts. The hearing-impaired have to challenge communication obstacles in a mostly hearing-capable culture. There are hundreds Sign Languages that are used all around the world today .The Sign Languages are established depending on the country and area of the deaf public. The aim of sign language recognition is to offer an effectual and correct tool to transcribe hand gesture into text. It can play a vital role in the communiqué between deaf and hearing society. Sign language recognition (SLR), as one of the significant research fields of human–computer interaction (HCI), has produced more and more interest in HCI society. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for sign language recognition in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system.

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