Detection and Recognition of Sino-Nom Characters on Woodblock-Printed Images

There are many documents written in Sino-Nom capturing a wide range of historical, political, and literary facets of Vietnamese culture. To contribute to the process of preserving those documents, we focus on the task of building Optical Character Recognition models for Sino-Nom characters using a semi-supervised approach. Also, we propose a pipeline for collecting needed Sino-Nom document images which are used for training and evaluating our models. Being evaluated on our collected dataset, our OCR baseline achieves F1 scores of over 0.97 in detection and top-l and top-5 accuracies of 80.1% and 90.1% in recognition, respectively.