American Sign Language Recognition System: An Optimal Approach

Full Text (PDF, 1138KB), PP.18-30

Views: 0 Downloads: 0


Shivashankara S 1,* Srinath S 1

1. Department of Computer Science & Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India

* Corresponding author.


Received: 8 Feb. 2018 / Revised: 6 Apr. 2018 / Accepted: 17 May 2018 / Published: 8 Aug. 2018

Index Terms

American Sign Language, Gesture Recognition, ASL Alphabets, ASL Numbers, Preprocessing, Region Properties


The Sign language is a visual language used by the people with the speech and hearing disabilities for communication in their daily conversation activities. It is completely an optical communication language through its native grammar, be unlike fundamentally from that of oral languages. In this research paper, presented an optimal approach, whose major objective is to accomplish the transliteration of 24 static sign language alphabets and numbers of American Sign Language into humanoid or machine decipherable English manuscript. Pre-processing operations of the signed input gesture are done in the first phase. In the next phase, the various region properties of pre-processed gesture image is computed. In the final phase, based on the properties calculated of earlier phase, the transliteration of signed gesture into text has been carried out. This paper also presents the statistical result evaluation with the comparative graphical depiction of existing techniques and proposed technique.

Cite This Paper

Shivashankara S, Srinath S, " American Sign Language Recognition System: An Optimal Approach ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.8, pp. 18-30, 2018. DOI: 10.5815/ijigsp.2018.08.03


[1]B M Chethana Kumara, H S Nagendraswamy and R Lekha Chinmayi, “Spatial Relationship Based Features for Indian Sign Language Recognition”, International Journal of Computing, Communications & Instrumentation Engineering, Vol. 3, Issue 2, ISSN 2349-1469, 2016.

[2]Srinath S, Ganesh Krishna Sharma, “Classification approach for Sign Language Recognition”, International Conference on Signal, Image Processing, Communication & Automation, 2017.

[3]Shivashankara S, Srinath S,  “A comparative Study of Various Techniques and Outcomes of Recognizing American Sign Language: A Review”,  International Journal of Scientific Research Engineering & Technology (IJSRET), Vol. 6, Issue 9, pp.1013-1023, 2017.

[4]Shivashankara S, Srinath S, “A Review on Vision Based American Sign Language Recognition, its Techniques, and Outcomes”, 7th IEEE International Conference on Communication Systems and Network Technologies (CSNT-2017), pp.293-299, 2017.

[5]Helen Cooper, Brian Holt, Richard Bowden, “Sign Language Recognition”, 

[6]Dr. Roger Sapsford, Victor Jupp, "Data Collection and Analysis", 2nd Edition, Sage Publishing Ltd, 2006.





[11]Jayshree R. Pansare, Maya Ingle, “Vision-Based Approach for American Sign Language Recognition Using Edge Orientation Histogram”, International Conference on Image, Vision and Computing, pp.86-90, 2016.

[12]Matheesha Fernando, Janaka Wijayanayaka, “Low cost approach for Real Time Sign Language Recognition”, 8th International Conference on Industrial and Information Systems, pp.637-642, 2013.

[13]Nagaraj N. Bhat, Y V Venkatesh, Ujjwal Karn, Dhruva Vig, “Hand Gesture Recognition using Self Organizing Map for Human Computer Interaction”, International Conference on Advances in Computing, Communications and Informatics, pp.734-738, 2013.

[14]Fahad Ullah, “American Sign Language Recognition System for Hearing Impaired People Using Cartesian Genetic Programming”, 5th International Conference on Automation, Robotics and Applications, pp.96-99, 2011.

[15]Asha Thalange, Dr. S. K. Dixit, “COHST and Wavelet Features Based Static ASL Numbers Recognition”, 2nd International Conference on Intelligent Computing, Communication & Convergence (Elsevier), pp.455-460, 2016.

[16]Asha Thalange, Shantanu Dixit, “Effect of thinning extent on ASL number recognition using open-finger distance feature measurement technique”, International Conference on Signal Processing And Communication Engineering Systems (SPACES), pp.39-43, 2015.

[17]Sharmila Konwar, Sagarika Borah, Dr.T Tuithung, “An American Sign Language Detection System using HSV Color Model and Edge Detection”, IEEE International Conference on Communication and Signal Processing, pp.743-747, 2014.

[18]Sruthi Upendran, Thamizharasi. A, “American Sign Language Interpreter System for Deaf and Dumb Individuals”, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014.

[19]Aditi Kalsh, N.S. Garewal,  “Sign Language Recognition for Deaf & Dumb”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 3, Issue 9, pp.103-106, 2013

[20]Sriparna Saha, Rimita Lahiri, Amit Konar, Atulya K. Nagar, “A Novel Approach To American Sign Language Recognition Using MAdaline Neural Network”, IEEE Symposium Series on Computational Intelligence (SSCI), 2016

[21]Geetha M, Rohit Menon, Suranya Jayan, Raju James, Janardhan G.V.V, “Gesture Recognition for American Sign Language with Polygon Approximation”, IEEE International Conference on Technology for Education, pp.241-245, 2011.

[22]Nachamai. M, “Alphabet Recognition of American Sign Language: A Hand Gesture Recognition Approach Using Sift Algorithm”, International Journal of Artificial Intelligence & Applications, Vol.4, No.1, pp.105-115, 2013.

[23]Davi Hirafuji Neiva, Cleber Zanchettin, “A Dynamic Gesture Recognition System to Translate Between Sign Languages in Complex Backgrounds”, 5th Brazilian Conference on Intelligent Systems, pp.421-426, 2016.

[24]Zhi-hua Chen, Jung-Tae Kim, Jianning Liang, Jing Zhang, and Yu-Bo Yuan,  “Real-Time Hand Gesture Recognition Using Finger Segmentation”, Hindawi Publishing Corporation, The Scientific World Journal, Vol 2014, Article ID 267872, pp.1-9, 2014.

[25]Suchin Adhan and Chuchart Pintavirooj, “Alphabetic Hand Sign Interpretation using Geometric Invariance”, IEEE International Conference on Biomedical Engineering, 2014.

[26]Taehwan Kim, Karen Livescu, Gregory Shakhnarovich, American Sign Language Finger spelling Recognition with phonological feature-based tandem models, IEEE workshop on Spoken Language Technology, pp.119-124, 2012.

[27]Gururaj P Surampalli, Dayanand J, Dhananjay M, An Analysis of Skin Pixel Detection using Different Skin Color Extraction Techniques, International Journal of Computer Applications, Vol. 54, No. 17, pp.1-5, 2012.

[28]Rafael C Gonzalez, Richard E Woods, "Digital Image Processing”, 2nd Edition, Prentice Hall, 2008.