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Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance

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@article{IJASEIT2746,
   author = {Dolly Indra and Sarifuddin Madenda and Eri Prasetyo Wibowo},
   title = {Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1644--1652},
   keywords = {BISINDO; segmentation; morphology; edge detection; contour following; chain code; Euclidean distance},
   abstract = {

In Indonesia, there are two forms of sign language practiced in the community, i.e., Indonesian sign language or known as BISINDO, and Indonesian sign language system or known as SIBI. In this study, we conduct research about recognition of Bisindo alphabets using contour chain code for the method of feature extraction and similarity of Euclidean distance for the method of recognition. The features used are the probability of chain code generated from contour following and the formation of chain code. The proposed method in this study consisted of five section, i.e., input test image, segmentation, edge detection, feature extraction and matching process of the alphabet. In the testing of the proposed method, we used 52 images of hand gestures used as test images. The images are in the form of static images and 26 images of hand gestures used as reference images which represent 26 alphabets BISINDO from A to Z, where the images stored in the database. The test images of different shapes and sizes with image references. For recognition, we do the matching between the probability of the test image chain code with the probability of the reference image chain code using Euclidean distance. The measurement result of Euclidean distance in this study was generated average accuracy rate of similarity above 94%. This indicates that the method proposed in this study was effective and produce the level of similarity of BISINDO alphabets was accurate.

},    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=2746},    doi = {10.18517/ijaseit.7.5.2746} }

EndNote

%A Indra, Dolly
%A Madenda, Sarifuddin
%A Wibowo, Eri Prasetyo
%D 2017
%T Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance
%B 2017
%9 BISINDO; segmentation; morphology; edge detection; contour following; chain code; Euclidean distance
%! Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance
%K BISINDO; segmentation; morphology; edge detection; contour following; chain code; Euclidean distance
%X 

In Indonesia, there are two forms of sign language practiced in the community, i.e., Indonesian sign language or known as BISINDO, and Indonesian sign language system or known as SIBI. In this study, we conduct research about recognition of Bisindo alphabets using contour chain code for the method of feature extraction and similarity of Euclidean distance for the method of recognition. The features used are the probability of chain code generated from contour following and the formation of chain code. The proposed method in this study consisted of five section, i.e., input test image, segmentation, edge detection, feature extraction and matching process of the alphabet. In the testing of the proposed method, we used 52 images of hand gestures used as test images. The images are in the form of static images and 26 images of hand gestures used as reference images which represent 26 alphabets BISINDO from A to Z, where the images stored in the database. The test images of different shapes and sizes with image references. For recognition, we do the matching between the probability of the test image chain code with the probability of the reference image chain code using Euclidean distance. The measurement result of Euclidean distance in this study was generated average accuracy rate of similarity above 94%. This indicates that the method proposed in this study was effective and produce the level of similarity of BISINDO alphabets was accurate.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2746 %R doi:10.18517/ijaseit.7.5.2746 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 5 %@ 2088-5334

IEEE

Dolly Indra,Sarifuddin Madenda and Eri Prasetyo Wibowo,"Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1644-1652, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.2746.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Indra, Dolly
AU  - Madenda, Sarifuddin
AU  - Wibowo, Eri Prasetyo
PY  - 2017
TI  - Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1644
EP  - 1652
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - BISINDO; segmentation; morphology; edge detection; contour following; chain code; Euclidean distance
N2  - 

In Indonesia, there are two forms of sign language practiced in the community, i.e., Indonesian sign language or known as BISINDO, and Indonesian sign language system or known as SIBI. In this study, we conduct research about recognition of Bisindo alphabets using contour chain code for the method of feature extraction and similarity of Euclidean distance for the method of recognition. The features used are the probability of chain code generated from contour following and the formation of chain code. The proposed method in this study consisted of five section, i.e., input test image, segmentation, edge detection, feature extraction and matching process of the alphabet. In the testing of the proposed method, we used 52 images of hand gestures used as test images. The images are in the form of static images and 26 images of hand gestures used as reference images which represent 26 alphabets BISINDO from A to Z, where the images stored in the database. The test images of different shapes and sizes with image references. For recognition, we do the matching between the probability of the test image chain code with the probability of the reference image chain code using Euclidean distance. The measurement result of Euclidean distance in this study was generated average accuracy rate of similarity above 94%. This indicates that the method proposed in this study was effective and produce the level of similarity of BISINDO alphabets was accurate.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2746 DO - 10.18517/ijaseit.7.5.2746

RefWorks

RT Journal Article
ID 2746
A1 Indra, Dolly
A1 Madenda, Sarifuddin
A1 Wibowo, Eri Prasetyo
T1 Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1644
OP 1652
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 BISINDO; segmentation; morphology; edge detection; contour following; chain code; Euclidean distance
AB 

In Indonesia, there are two forms of sign language practiced in the community, i.e., Indonesian sign language or known as BISINDO, and Indonesian sign language system or known as SIBI. In this study, we conduct research about recognition of Bisindo alphabets using contour chain code for the method of feature extraction and similarity of Euclidean distance for the method of recognition. The features used are the probability of chain code generated from contour following and the formation of chain code. The proposed method in this study consisted of five section, i.e., input test image, segmentation, edge detection, feature extraction and matching process of the alphabet. In the testing of the proposed method, we used 52 images of hand gestures used as test images. The images are in the form of static images and 26 images of hand gestures used as reference images which represent 26 alphabets BISINDO from A to Z, where the images stored in the database. The test images of different shapes and sizes with image references. For recognition, we do the matching between the probability of the test image chain code with the probability of the reference image chain code using Euclidean distance. The measurement result of Euclidean distance in this study was generated average accuracy rate of similarity above 94%. This indicates that the method proposed in this study was effective and produce the level of similarity of BISINDO alphabets was accurate.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2746 DO - 10.18517/ijaseit.7.5.2746