10 citations · 14 across the 5 of their papers we have counts for
7 papers
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models
Anna Scius-Bertrand, Michael Jungo, Lars Vögtlin +2
Classifying scanned documents is a challenging problem that involves image, layout, and text analysis for document understanding. Nevertheless, for certain benchmark datasets, nota…
Contrastive Learning for Character Detection in Ancient Greek Papyri
Vedasri Nakka, Andreas Fischer, Rolf Ingold +1
This thesis investigates the effectiveness of SimCLR, a contrastive learning technique, in Greek letter recognition, focusing on the impact of various augmentation techniques. We p…
Impact of Ground Truth Quality on Handwriting Recognition
Michael Jungo, Lars Vögtlin, Atefeh Fakhari +4
Handwriting recognition is a key technology for accessing the content of old manuscripts, helping to preserve cultural heritage. Deep learning shows an impressive performance in so…
DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis
Lars Vögtlin, Anna Scius-Bertrand, Paul Maergner +2
Deep learning methods have shown strong performance in solving tasks for historical document image analysis. However, despite current libraries and frameworks, programming an exper…
Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs
Lars Vögtlin, Manuel Drazyk, Vinaychandran Pondenkandath +2
We present a framework to generate synthetic historical documents with precise ground truth using nothing more than a collection of unlabeled historical images. Obtaining large lab…
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts
Michele Alberti, Lars Vögtlin, Vinaychandran Pondenkandath +3
This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in co…