PHD: Pixel-Based Language Modeling of Historical Documents
Submission Type: Regular Long Paper
Submission Track: Computational Social Science and Cultural Analytics
Submission Track 2: Language Modeling and Analysis of Language Models
Keywords: Visual language modelling, Historical documents, Multimodal models
TL;DR: We use pixel-based language models to train a general purpose model for historical documents that is OCR-free
Abstract: The digitisation of historical documents has provided historians with unprecedented research opportunities. Yet, the conventional approach to analysing historical documents involves converting them from images to text using OCR, a process that overlooks the potential benefits of treating them as images and introduces high levels of noise. To bridge this gap, we take advantage of recent advancements in pixel-based language models trained to reconstruct masked patches of pixels instead of predicting token distributions. Due to the scarcity of real historical scans, we propose a novel method for generating synthetic scans to resemble real historical documents. We then pre-train our model, PHD, on a combination of synthetic scans and real historical newspapers from the 1700-1900 period. Through our experiments, we demonstrate that PHD exhibits high proficiency in reconstructing masked image patches and provide evidence of our model's noteworthy language understanding capabilities. Notably, we successfully apply our model to a historical QA task, highlighting its usefulness in this domain.
Submission Number: 1679
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