• Diksha Bendale
• Kunal Goel
• Rutuja chaudhari
• Supriya Thorat

Inability to talk is taken into account to be a real disability. People with this disability use different modes to speak with others, there are several methods available for his or her communication, one such common method of communication is dactylology. Sign language allows people to communicate with human body language; each alphabet has a set of human hand signs representing a particular expression. The purpose of the paper is to convert sign language to text and sentences with human gesture classification. Sign Language Recognition is one among the foremost growing fields of research. Many new techniques have been developed recently within this area. Sign Language is especially used for the communication of deaf-mute people. This paper shows the sign language recognizing 26 hand gestures in Sign language using CONVOLUTIONAL NEURAL NETWORK. The proposed system contains three modules such as Image capture module which contains image capturing using webcam and database formation, the feature extraction module having color detection and shape detection, the pattern generation module having trained model and sign recognition. The final output will be predicted text from the sign expressed by the user

Sign Language, Hand Gesture Recognition, Image Processing

"Sign language Conversion For Hearing And Speech Impaired", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.d137-d142, June-2021, Available :http://www.jetir.org/papers/JETIR2106420.pdf

"Sign language Conversion For Hearing And Speech Impaired", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppd137-d142, June-2021, Available at : http://www.jetir.org/papers/JETIR2106420.pdf