Word spotting is popularly used for digitisation and transcription of historical handwritten documents. Recently, deep learning based methods have dominated the current state-of-the-art in learning-based word spotting. However, deep learning architectures such as Convolutional Neural Networks (CNNs) require a large amount of training data, and suﬀer from translation invariance. Capsule Networks (CapsNet) have been recently introduced as a data-eﬃcient alternative to CNNs. This work explores the applicability of CapsNets for segmentation-based word spotting, and is the ﬁrst such eﬀort in the Handwritten Text Recognition (HTR) com-munity to the best of authors’ knowledge. The eﬀectiveness of CapsNets will be empirically evaluated on well-known historical handwritten datasets using standard evaluation measures. The impact of varying amounts of training data on the recognition performance will be inves-tigated, along with a comparison with the state-of-the-art methods.