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In the world of signing and gestures, lots of analysis work has been done over the past three decades. This has led to a gradual transition from isolated to continuous, and static to dynamic gesture recognition for operations on a restricted vocabulary. In gift state of affairs, human machine interactive systems facilitate communication between the deaf, and hearing impaired in universe things. So as to boost the accuracy of recognition, several researchers have deployed strategies like HMM, Artificial Neural Networks, and Kinect platform. Effective algorithms for segmentation, classification, pattern matching and recognition have evolved. The most purpose of this paper is to investigate these strategies and to effectively compare them, which can alter the reader to succeed in associate in nursing optimum resolution. This creates each, challenges and opportunities for signing recognition connected analysis. Normal 0 false false false DE JA X-NONE /* Style Definitions */  table.MsoNormalTable  {mso-style-name:"Table Normal";  mso-tstyle-rowband-size:0;  mso-tstyle-colband-size:0;  mso-style-noshow:yes;  mso-style-priority:99;  mso-style-parent:"";  mso-padding-alt:0in 5.4pt 0in 5.4pt;  mso-para-margin:0in;  mso-para-margin-bottom:.0001pt;  mso-pagination:widow-orphan;  font-size:12.0pt;  font-family:"Cambria","serif";  mso-ascii-font-family:Cambria;  mso-ascii-theme-font:minor-latin;  mso-hansi-font-family:Cambria;  mso-hansi-theme-font:minor-latin;  mso-ansi-language:DE;  mso-fareast-language:JA;}  ANFIS Based Methodology for Sign Language Recognition and Translating to Number in Kannada Language Normal 0 false false false DE JA X-NONE