Thông tin chung
  English
  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác
  Tài liệu tham khảo
  Hiệu chỉnh
 
Số người truy cập: 106,666,105

 Vietnamese Sign Language Recognition using Cross Line Descriptors and Invariant Moments
cvs weekly sale cvs print prescription savings cards
Tác giả hoặc Nhóm tác giả: Nguyen Trong Nguyen; Huynh Huu Hung; Jean Meunier
Nơi đăng: International Journal of Advanced Research in Computer Science (IJARCS); Số: 4(11);Từ->đến trang: 26-31;Năm: 2013
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Sign language is the primary language used by the deaf community in order to convey information through gestures instead of words. Many researches about this language have been done, and they play an important role in life. Currently, most of the hard-of-hearings in Vietnam are facing many difficulties in community integration because of their limited ability. So we propose an approach which can recognize Vietnamese sign language, based on digital image processing combined with a machine learning method. After pre-processing, we use a combination of cross lines descriptors and invariant moments to extract the features, and then the gesture is recognized using support vector machines. This is one of the few studies on sign language applied to Vietnamese alphabet (the number of words is higher and more complex than international standards with several accented letters). The proposed approach has been tested with high accuracy and is promising.
ABSTRACT
Sign language is the primary language used by the deaf community in order to convey information through gestures instead of words. Many researches about this language have been done, and they play an important role in life. Currently, most of the hard-of-hearings in Vietnam are facing many difficulties in community integration because of their limited ability. So we propose an approach which can recognize Vietnamese sign language, based on digital image processing combined with a machine learning method. After pre-processing, we use a combination of cross lines descriptors and invariant moments to extract the features, and then the gesture is recognized using support vector machines. This is one of the few studies on sign language applied to Vietnamese alphabet (the number of words is higher and more complex than international standards with several accented letters). The proposed approach has been tested with high accuracy and is promising.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn