ASL Recognition Quality Analysis Based on Sensory Gloves and MLP Neural Network


  • Firas A. Raheem University of Technology
  • Hadeer A.Raheem


ASL, Artificial neural network, forward kinematics, inverse kinematics, deaf, DOF.


A simulated human hand model has been built using a virtual reality program which converts printed letters into a human hand figure that represents American Sign Language (ASL), this program was built using forward and inverse kinematics equations of a human hand. The inputs to the simulation program are normal language letters and the outputs are the human hand figures that represent ASL letters. In this research, a hardware system was designed to recognize the human hand manual alphabet of the ASL utilizing a hardware glove sensor design and using artificial neural network for enhancing the recognition process of ASL and for converting the ASL manual alphabet into printed letters. The hardware system uses flex sensors which are positioned on gloves to obtain the finger joint angle data when shown each letter of ASL. In addition, the system uses DAQ 6212 to interface the sensors and the PC. We trained and tested our hardware system for (ASL) manual alphabet words and names recognition and the recognition results have the accuracy of 90.19% and the software system for converting printed English names and words into (ASL) have 100% accuracy.


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How to Cite

Raheem, F. A., & A.Raheem, H. (2018). ASL Recognition Quality Analysis Based on Sensory Gloves and MLP Neural Network. American Scientific Research Journal for Engineering, Technology, and Sciences, 47(1), 1–20. Retrieved from