Few-shot symbol classification via self-supervised learning and nearest neighbor

•Self-Supervised Learning for few-shot classification in Document Analysis.•Neural embedded spaces obtained from unlabeled documents in a self-supervised manner.•Inference with few labeled data samples considering the k-Nearest Neighbor rule.•Experimentation comprises four heterogenous corpora and five classification schemes.•Proposal significantly improves performance rates of reference strategie.