Abstract: In our day to day life we use devnagari to communicate with each other verbally. There are many people in our country who still like to write their documents in devnagari only. In our project we recognizing devnagari as well as joint devnagari characters. The character images in our dataset are imposed by joint characters, this particular aspect leads to various conflicting behaviors of the recognition algorithm which in turn reduces the accuracy of recognition. The training of joint devnagari character image samples are carried out by using one of the deep convolution neural networks known as CNN. The handwritten datasets is collected artificially from users in the age range of 18–21, 22–25, and 26–30. It consists of joint devnagari text that are used to evaluate the experiment's performance. The datasets are comprised of many classes. Those classes include devnagari characters, devnagari digits as well as joint devnagari characters. After performing essential steps. It is observed that the performance of CNN Classifiers like Random Forest is overall high. An overall accuracy of 94% is achieved during the recognition of devnagari character set and an accuracy of over 90% is accomplished with respect to handwritten data samples with training and testing proportions of 70% and 30% in both of the cases for the number of classes of over 58