  In this work, a novel deep learning technique for the recognition of
handwritten Bangla isolated compound character is presented and a new benchmark
of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy
layer wise training of Deep Neural Network has helped to make significant
strides in various pattern recognition problems. We employ layerwise training
to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and
augment the training process with the RMSProp algorithm to achieve faster
convergence. We compare results with those obtained from standard shallow
learning methods with predefined features, as well as standard DCNNs.
Supervised layerwise trained DCNNs are found to outperform standard shallow
learning models such as Support Vector Machines as well as regular DCNNs of
similar architecture by achieving error rate of 9.67% thereby setting a new
benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%,
representing an improvement of nearly 10%.
