Few-Shot Learning for Word Recognition in Handwritten Seventeenth-Century Spanish American Notary Records.

Historical records are invaluable sources of information that provide insights into multiple aspects of past events and societies. The analysis of historical records using deep learning poses critical challenges such as the lack of sufficient labeled data and at times the poor quality of scanned images. In this paper, we propose SpanishFSL, a few-shot learning (FSL) approach for word recognition in 17th-century handwritten Spanish American notary records. SpanishFSL draws inspiration from a zero-shot learning approach developed for image classification. It leverages an autoencoder to construct class-attribute signatures to effectively bridge the gap between seen and unseen classes. This enables SpanishFSL to generalize and accurately recognize words not present in the training set. Our labeled dataset was prepared by paleography experts using a subset of the notary records drafted by two notaries. Through experimental evaluation, we observed that SpanishFSL can outperform other FSL classifiers in terms of word recognition accuracy.