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A Sign Language to Text Converter Using Leap Motion

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@article{IJASEIT1252,
   author = {Fazlur Rahman Khan and Huey Fang Ong and Nurhidayah Bahar},
   title = {A Sign Language to Text Converter Using Leap Motion},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {1089--1095},
   keywords = {Sign Language to Text; Leap Motion, Geometric Template Matching; Artificial Neural Network; Cross Correlation; American Sign Language},
   abstract = {This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1252},
   doi = {10.18517/ijaseit.6.6.1252}
}

EndNote

%A Khan, Fazlur Rahman
%A Ong, Huey Fang
%A Bahar, Nurhidayah
%D 2016
%T A Sign Language to Text Converter Using Leap Motion
%B 2016
%9 Sign Language to Text; Leap Motion, Geometric Template Matching; Artificial Neural Network; Cross Correlation; American Sign Language
%! A Sign Language to Text Converter Using Leap Motion
%K Sign Language to Text; Leap Motion, Geometric Template Matching; Artificial Neural Network; Cross Correlation; American Sign Language
%X This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1252
%R doi:10.18517/ijaseit.6.6.1252
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Fazlur Rahman Khan,Huey Fang Ong and Nurhidayah Bahar,"A Sign Language to Text Converter Using Leap Motion," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1089-1095, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1252.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Khan, Fazlur Rahman
AU  - Ong, Huey Fang
AU  - Bahar, Nurhidayah
PY  - 2016
TI  - A Sign Language to Text Converter Using Leap Motion
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 1089
EP  - 1095
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Sign Language to Text; Leap Motion, Geometric Template Matching; Artificial Neural Network; Cross Correlation; American Sign Language
N2  - This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1252
DO  - 10.18517/ijaseit.6.6.1252

RefWorks

RT Journal Article
ID 1252
A1 Khan, Fazlur Rahman
A1 Ong, Huey Fang
A1 Bahar, Nurhidayah
T1 A Sign Language to Text Converter Using Leap Motion
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 1089
OP 1095
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Sign Language to Text; Leap Motion, Geometric Template Matching; Artificial Neural Network; Cross Correlation; American Sign Language
AB This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1252
DO  - 10.18517/ijaseit.6.6.1252