Transformer-based HTR for Historical Documents
Phillip Benjamin Ströbel, Simon Clematide, Martin Volk, Tobias Hodel
Abstract
We apply the TrOCR framework to real-world, historical manuscripts and show that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has been trained on English only, but it can adapt to other languages that use the Latin alphabet fairly easily and with little training material. We compare TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such systems. This finding is essential since Transkribus performs best when it has access to baseline information, which is not needed at all to fine-tune TrOCR.
Related Papers
RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction2025-07-18Disentangling coincident cell events using deep transfer learning and compressive sensing2025-07-17Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows2025-07-16Robust-Multi-Task Gradient Boosting2025-07-15Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift2025-07-12The Bayesian Approach to Continual Learning: An Overview2025-07-11Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol2025-07-08Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving2025-07-08