  Convolutional neural networks(CNNs) has become one of the primary algorithms
for various computer vision tasks. Handwritten character recognition is a
typical example of such task that has also attracted attention. CNN
architectures such as LeNet and AlexNet have become very prominent over the
last two decades however the spatial invariance of the different kernels has
been a prominent issue till now. With the introduction of capsule networks,
kernels can work together in consensus with one another with the help of
dynamic routing, that combines individual opinions of multiple groups of
kernels called capsules to employ equivariance among kernels. In the current
work, we have implemented capsule network on handwritten Indic digits and
character datasets to show its superiority over networks like LeNet.
Furthermore, it has also been shown that they can boost the performance of
other networks like LeNet and AlexNet.
