Handwritten Devanagari Script Recognition has a lot of applications in the field of document processing, automation of postal services, automated cheque processing and so on. Several approaches have been proposed and experimented in the past depending on the type of features extracted and the ways of extracting them. In this paper, we proposed the use of Capsule Neural Networks (CapsNet) for the recognition of Handwritten Devanagari script, which is an advancement over the Convolutional Neural Networks (CNN) in terms of spatial relationships between the features. Capsule Neural Networks follow the principle of equivariance unlike the convolutional neural networks which follow the invariance property. CapsNet uses the dynamic routing by agreement method for passing data to higher capsules. CapsNet uses vector format for data representation. It can recognize similar characters in a more efficient manner as compared to CNN. Thus by using the advantages of CapsNet we are aiming to achieve better classification rate. We collected 100 samples of each of the 48 Devanagari characters and 10 Devanagari digits, and performed scaling, rotation and mirroring operations on these images. Hence, our dataset consists of total 29000 images.

Capsule Networks, Dynamic routing, Devanagari Script Recognition, Convolutional Neural Networks

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