Real Time Sign Language Recognition using the Leap Motion Controller

被引:0
|
作者
Naglot, Deepali [1 ]
Kulkarni, Milind [1 ]
机构
[1] Vishwakarma Inst Technol, Dept CSE IT, Pune, Maharashtra, India
关键词
Leap Motion Controller (LMC); American Sign Language (ASL); Sign Language; Multi-Layer Perceptron (MLP);
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Hearing and speech impaired people use Sign Language to convey their message to normal people. Sign Language has evolved as one of the major areas of research and study in computer vision. Researchers in sign language recognition used different input devices such as data gloves, web camera, depth camera, color camera, Microsoft's Kinect sensor, etc. to capture hand signs. In this paper we display the importance of American Sign Language and proposed technique for classification and their efficient results. American Sign Language uses only one hand to display the gestures and thus makes it easy for interpretation and understanding. The signs are captured using new digital sensor called "Leap Motion Controller". LMC is 3D non-contact motion sensor which can tracks and detects hands, fingers, bones and finger-like objects. Proposed system used Multi-Layer Perceptron (MLP) neural network with Back Propagation (BP) algorithm to build a classification model which takes feature set as input. Multi-Layer Perceptron (MLP) neural network used to recognize different signs. We have considered 26 different alphabets of American Sign Language. Multi-Layer Perceptron (MLP) is executed on a dataset of total 520 samples (consisting of 20 samples of each alphabet). Recognition rate of proposed system is 96.15%.
引用
收藏
页码:837 / 841
页数:5
相关论文
共 50 条
  • [1] Indonesian Sign Language Recognition Using Leap Motion Controller
    Wibowo, Midarto Dwi
    Nurtanio, Ingrid
    Ilham, Amil Ahmad
    [J]. PROCEEDINGS OF 2017 11TH INTERNATIONAL CONFERENCE ON INFORMATION & COMMUNICATION TECHNOLOGY AND SYSTEMS (ICTS), 2017, : 67 - 71
  • [2] Arabic Sign Language Recognition using the Leap Motion Controller
    Mohandes, M.
    Aliyu, S.
    Deriche, M.
    [J]. 2014 IEEE 23RD INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2014, : 960 - 965
  • [3] Recognition of Continuous Sign Language Alphabet Using Leap Motion Controller
    Cohen, Miri Weiss
    Ben Zikri, Nir Nir
    Velkovich, Alexander
    [J]. 2018 11TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION (HSI), 2018, : 193 - 199
  • [4] ANN based Indian Sign Language Numerals Recognition using the Leap Motion Controller
    Naglot, Deepali
    Kulkarni, Milind
    [J]. 2016 INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT), VOL 2, 2016, : 73 - 78
  • [5] American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach
    Chong, Teak-Wei
    Lee, Boon-Giin
    [J]. SENSORS, 2018, 18 (10)
  • [6] Real-Time Recognition of Sign Language Gestures and Air-Writing using Leap Motion
    Kumar, Pradeep
    Saini, Rajkumar
    Behera, Santosh Kumar
    Dogra, Debi Prosad
    Roy, Partha Pratim
    [J]. PROCEEDINGS OF THE FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS - MVA2017, 2017, : 157 - 160
  • [7] Real-Time Grasp Type Recognition Using Leap Motion Controller
    Zou, Yuanyuan
    Liu, Honghai
    Zhang, Jilong
    [J]. INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III, 2019, 11742 : 472 - 480
  • [8] Arabic sign language recognition using Ada-Boosting based on a leap motion controller
    Hisham B.
    Hamouda A.
    [J]. International Journal of Information Technology, 2021, 13 (3) : 1221 - 1234
  • [9] American Sign Language Recognition Using Leap Motion Sensor
    Chuan, Ching-Hua
    Regina, Eric
    Guardino, Caroline
    [J]. 2014 13TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2014, : 541 - 544
  • [10] A Chinese Sign Language Recognition System Using Leap Motion
    Xue, Yaofeng
    Gao, Shang
    Sun, Huali
    Qin, Wei
    [J]. 2017 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY AND VISUALIZATION (ICVRV 2017), 2017, : 180 - 185