In this digital era, most important thing is to deal with digital documents, organizations using handwritten documents for storing their information can use handwritten character recognition to convert this information into digital. Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human-robot interaction, automatic data entry for business documents, etc. It is very difficult to recognize Devanagari character due to presence of header line, characters that are attached together and similarity of shapes of multiple characters. In this project the recognition of handwritten Devanagari characters is done using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. I experimented on the data set of DHCP provided by ISI. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favourable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.