25 citations · 25 across the 4 of their papers we have counts for
11 papers
Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs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We present a framework to generate synthetic historical documents with precise ground truth using nothing more than a collection of unlabeled historical images.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…
Improving Reproducible Deep Learning Workflows with DeepDIVA
Michele Alberti, Vinaychandran Pondenkandath, Lars Vögtlin +3
The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results…
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
Joel Niklaus, Michele Alberti, Vinaychandran Pondenkandath +2
In the last decades we have witnessed the success of applications of Artificial Intelligence to playing games. In this work we address the challenging field of games with hidden in…
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
Linda Studer, Michele Alberti, Vinaychandran Pondenkandath +5
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated…
Leveraging Random Label Memorization for Unsupervised Pre-Training
Vinaychandran Pondenkandath, Michele Alberti, Sammer Puran +2
We present a novel approach to leverage large unlabeled datasets by pre-training state-of-the-art deep neural networks on randomly-labeled datasets. Specifically, we train the neur…