ESG Accountability Made Easy: DocQA at Your Service

no code implementations • 30 Nov 2023 • Lokesh Mishra, Cesar Berrospi, Kasper Dinkla, Diego Antognini, Francesco Fusco, Benedikt Bothur, Maksym Lysak, Nikolaos Livathinos, Ahmed Nassar, Panagiotis Vagenas, Lucas Morin, Christoph Auer, Michele Dolfi, Peter Staar

We present Deep Search DocQA.

Question Answering

ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents

no code implementations • 24 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 implementations • 5 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.

TableFormer: Table Structure Understanding with Transformers

1 code implementation • CVPR 2022 • Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar

In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders.

object-detection Object Detection +1

Robust PDF Document Conversion Using Recurrent Neural Networks

no code implementations • 18 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