• Aman Jain
• Simran Sharma

In this paper, convolutional neural networks have been used for the recognition of numbers in sign language. The efficiency of recognition, highly depends on the choice of the neural network architecture. This paper, focuses on determining the best architecture for recognition of numbers in sign language. This has been done by conducting training experiments on “Sign Language Digits” Dataset and comparing of results using accuracy metrics.

Convolutional neural networks; architecture; accuracy; compare.

"COMPARISON OF DIFFERENT CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR THE RECOGNITION OF NUMBERS IN SIGN LANGUAGE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.557-564, June 2019, Available :http://www.jetir.org/papers/JETIR1906W76.pdf

"COMPARISON OF DIFFERENT CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR THE RECOGNITION OF NUMBERS IN SIGN LANGUAGE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp557-564, June 2019, Available at : http://www.jetir.org/papers/JETIR1906W76.pdf