Hand gesture recognition as the computer interpretation of human hand gestures used to assist people with disabilities. The texture-based feature descriptors of hand sign images do not have sufficient distinct features for recognition due to the change in geometric viewpoint and illumination invariant images. For instance, texture-based descriptors such as local binary patterns are sensitive to noise images. To overcome this problem, this paper proposes a novel global information extraction called contrast information fractal dimension (CIFD), which is based on contrast entropy and fractal dimension. The proposed CIFD method is used to obtain distinct features by analysing the edge quantity of hand sign images through measuring the information content, using non-linear filtering techniques and contrast entropy based on Weber and Devries-Rose contrast measurement. The extracted information from the hand edge images is applied to the convolutional neural network (CNN) for better hand sign recognition. The CNN is used to learn complex and non-linear relationships in static hand gesture images for recognition. The efficiency of the proposed system is verified by comparing various texture-based feature descriptors using standard databases. The hand sign recognition accuracy of the Jochen Triesch database for uniform and dark background environments are 99.50% and 95% and that of the National University of Singapore database (NUS-I) for black-white and colour background environments are 92.50% and 95% respectively.