India has the largest deaf population in the world and sign language is the principal medium for such persons to share information with normal people and among themselves. Yet, normal people do not have any knowledge of such language. As a result, there is a huge communication barrier between normal and deaf-dumb persons. Again, sign language interpreters are not easily available and it is a very costly solution for a long period. The sign language recognition system reduces the communication gaps between normal and deaf-dumb persons. The methodologies to recognize Indian sign language are recently in the developing stage and there is no approach to recognize signs in real-time. Here, we have proposed a fingerspelling recognition system of static signs for the Indian sign language alphabet using convolutional neural networks combined with data augmentation, batch normalization, dropout, stochastic pooling, and diffGrad optimizer. To continue the research, a total of 62,400 images of 26 static signs have been taken from various users. The proposed method achieves the highest training and validation accuracy of 99.76% and 99.64%, respectively , that outperforms other examined systems.