The sign language, considered as the main language for deaf and hard of hearing, uses manual communication and body language to convey expressions and plays a major role in developing an identity. Nowadays, sign language recognition is an emerging field of research to improve interaction with the deaf community. The automatic recognition of American, British, and French sign languages with high accuracy has been reported in the literature. Even though, Bangla is one of the mostly spoken languages in the world, no significant research on Bangla sign language recognition can be found in the literature. The main reason for this lagging might be due to the unavailability of a Bangla sign language dataset. In this study, we have presented a large dataset of Bangla sign language consisting of both alphabets and numerals. The dataset was composed of 7052 samples representing 10 numerals and 23864 samples correspond to the 35 basic characters of the alphabet. Finally, the performance of a convolutional neural network in the recognition of numerals and alphabet separately, and in mixing of them, has been evaluated on the developed dataset using 10-fold cross-validation. The proposed method provided an average recognition accuracy of 99.83%, 100%, and 99.80%, respectively for basic characters, numerals, and for their combined usage.