Recent progress in the areas of Artificial Intelligence (AI) and Machine Learning (ML) are tremendous and amazing. Almost monthly we see reports announcing breakthroughs in different technological aspects of AI.
As an organization focussing on research and development, we can look back on an increasing number of publications and awards.

Publications
We aim to push the state-of-the-art for problems such as automatic text recognition (ATR), language modeling (LM), named entity recognition (NER), visual question answering (VQA) and image segmentation (IS) even beyond human performance.
Our team of experienced AI researchers is working with and improving techniques such as:
- fully convolutional neural networks
- attention-based recurrent free models as well as in combination with recurrent models
- graph neural networks
- neural memory techniques
- unsupervised and self-supervised pre-training strategies
- improved learning strategies
Authors: Christian Reul (University of Würzburg), Christoph Wick (PLANET AI GmbH), Maximilian Nöth, Andreas Büttner, Maximilian Wehner (all University of Würzburg), Uwe Springmann (LMU München)
Series: ICDAR 2021
Pages: 112 – 126
DOI: 10.1007/978-3-030-86334-0_8
Authors: Christoph Wick (PLANET AI GmbH), Jochen Zöllner (PLANET AI GmbH, University of Rostock), Tobias Grüning (PLANET AI GmbH)
Series: ICDAR 2021
Pages: 112 – 126
In this paper, we propose a novel method for Automatic Text Recognition (ATR) on early printed books. Our approach significantly reduces the Character Error Rates (CERs) for book-specific training when only a few lines of Ground Truth (GT) are available and considerably outperforms previous methods. An ensemble of models is trained simultaneously by optimising each one independently but also with respect to a fused output obtained by averaging the individual confidence matrices. Various experiments on five early printed books show that this approach already outperforms the current state-of-the-art by up to 20% and 10% on average. Replacing the averaging of the confidence matrices during prediction with a con dence-based voting boosts our results by an additional 8% leading to a total average improvement of about 17%.
Authors: Christoph Wick, Benjamin Kühn, Gundram Leifert (all PLANET AI GmbH), Konrad Sperfeld (CITlab, University of Rostock), Jochen Zöllner (PLANET AI GmbH, University of Rostock), Tobias Grüning (PLANET AI GmbH)
Journal: The Journal of Open Source Software (JOSS)
DOI: 10.21105/joss.03297