3 citations · 4 across the 6 of their papers we have counts for
6 papers
CM1 -- A Dataset for Evaluating Few-Shot Information Extraction with Large Vision Language Models
Fabian Wolf, Oliver Tüselmann, Arthur Matei +3
The automatic extraction of key-value information from handwritten documents is a key challenge in document analysis. A reliable extraction is a prerequisite for the mass digitizat…
Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation
Fabian Wolf, Gernot A. Fink
Performances of Handwritten Text Recognition (HTR) models are largely determined by the availability of labeled and representative training samples. However, in many application sc…
Recognition-free Question Answering on Handwritten Document Collections
Oliver Tüselmann, Friedrich Müller, Fabian Wolf +1
In recent years, considerable progress has been made in the research area of Question Answering (QA) on document images. Current QA approaches from the Document Image Analysis comm…
Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling
Fabian Wolf, Gernot A. Fink
Word spotting is a popular tool for supporting the first exploration of historic, handwritten document collections. Today, the best performing methods rely on machine learning tech…
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets
Fabian Wolf, Philipp Oberdiek, Gernot A. Fink
In recent years, convolutional neural networks (CNNs) took over the field of document analysis and they became the predominant model for word spotting. Especially attribute CNNs, w…
Exploring Architectures for CNN-Based Word Spotting
Eugen Rusakov, Sebastian Sudholt, Fabian Wolf +1
The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query. The recent past has seen attribute-based Convolut…