# Handwritten Indic Character Recognition using Capsule Networks

## TL;DR

This paper demonstrates that capsule networks outperform traditional CNNs like LeNet and AlexNet in handwritten Indic character recognition, showing improved accuracy and the ability to enhance other models' performance.

## Contribution

The study applies capsule networks to handwritten Indic characters, showing their superiority and ability to boost existing CNN architectures.

## Key findings

- Capsule networks outperform LeNet in Indic character recognition.
- Capsule networks can enhance the performance of CNNs like LeNet and AlexNet.
- Capsule networks demonstrate better spatial invariance than traditional CNNs.

## Abstract

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.