Papers by Jonathan Allen
Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription (2022.findings-naacl)
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| Challenge: | Document transcription models are limited by extremely varied style and content across domains. |
| Approach: | They propose a self-supervised approach for learning rich visual representations for both handwritten and printed historical document transcription using a heterogeneous set of handwritten Islamicate manuscript images and early modern English printed documents. |
| Outcome: | The proposed model improves on a supervised model with as few as 30 line image transcriptions on two languages with a single line of image training. |