Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription
Nikolai Vogler, Jonathan Allen, Matthew Miller, Taylor Berg-Kirkpatrick
Abstract
We present a self-supervised pre-training approach for learning rich visual language representations for both handwritten and printed historical document transcription. After supervised fine-tuning of our pre-trained encoder representations for low-resource document transcription on two languages, (1) a heterogeneous set of handwritten Islamicate manuscript images and (2) early modern English printed documents, we show a meaningful improvement in recognition accuracy over the same supervised model trained from scratch with as few as 30 line image transcriptions for training. Our masked language model-style pre-training strategy, where the model is trained to be able to identify the true masked visual representation from distractors sampled from within the same line, encourages learning robust contextualized language representations invariant to scribal writing style and printing noise present across documents.
BibTeX
@inproceedings{vogler-etal-2022-lacuna,
title = "Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription",
author = "Vogler, Nikolai and
Allen, Jonathan and
Miller, Matthew and
Berg-Kirkpatrick, Taylor",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.15/",
doi = "10.18653/v1/2022.findings-naacl.15",
pages = "206--216"
}