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Character recognition is a critical module in applications such as document scanning and optical character recognition. With the emergence of deep learning techniques, languages like English have achieved high classification accuracies. However, the applicability of those deep learning methods is constrained in low resource languages, because of the lack of well-developed datasets. This study has focused on implementing a viable method for classification of handwritten characters in low resource languages. Due to the restrictions on the size of available dataset, this problem is modelled as a one-shot learning problem and solved using Siamese networks based on Capsule networks. Siamese network is a de facto type of network use in one-shot learning, but when it comes to image-related tasks, they still need a large number of training dataset. However, the use of Capsule layers-based Siamese network, which can mitigate information losses in Convolutional neural networks allowed to train a Siamese network with a small number of parameters, datasets and get on par performance as a convolutional network. This model is tested with Omniglot dataset and achieved 30–85% accuracy for different alphabets. Further, the model has shown a classification accuracy of 74.5% for MNIST dataset.