Search Results for author: Christoph Auer

Found 11 papers, 4 papers with code

ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents

no code implementations24 May 2023 Christoph Auer, Ahmed Nassar, Maksym Lysak, Michele Dolfi, Nikolaos Livathinos, Peter Staar

The results demonstrate substantial progress towards achieving robust and highly generalizing methods for document layout understanding.

Data Augmentation

Optimized Table Tokenization for Table Structure Recognition

no code implementations5 May 2023 Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, Peter Staar

The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average.

FETA: Towards Specializing Foundation Models for Expert Task Applications

1 code implementation8 Sep 2022 Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Kate Saenko, PeterW. J. Staar, Rogerio Feris, Leonid Karlinsky

However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e. g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training.

Domain Generalization Image Retrieval +6

DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis

1 code implementation2 Jun 2022 Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S Nassar, Peter W J Staar

Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.

Document Layout Analysis Object Detection

Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness

1 code implementation1 Jun 2022 Christoph Auer, Michele Dolfi, André Carvalho, Cesar Berrospi Ramis, Peter W. J. Staar

In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure.

document understanding Optical Character Recognition +1

Robust PDF Document Conversion Using Recurrent Neural Networks

no code implementations18 Feb 2021 Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, Peter Staar

In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature.

Feature Engineering Information Retrieval +1

An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries

no code implementations19 Jul 2019 Matteo Manica, Christoph Auer, Valery Weber, Federico Zipoli, Michele Dolfi, Peter Staar, Teodoro Laino, Costas Bekas, Akihiro Fujita, Hiroki Toda, Shuichi Hirose, Yasumitsu Orii

Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.

Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]

no code implementations15 May 2018 Peter W J Staar, Michele Dolfi, Christoph Auer, Costas Bekas

We present a platform to ingest documents at scale which is powered by Machine Learning techniques and allows the user to train custom models on document collections.

BIG-bench Machine Learning

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