Search Results for author: Ahmed Nassar

Found 8 papers, 3 papers with code

MolGrapher: Graph-based Visual Recognition of Chemical Structures

1 code implementation ICCV 2023 Lucas Morin, Martin Danelljan, Maria Isabel Agea, Ahmed Nassar, Valery Weber, Ingmar Meijer, Peter Staar, Fisher Yu

In addition, we introduce a large-scale benchmark of annotated real molecule images, USPTO-30K, to spur research on this critical topic.

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.

Multilevel sentiment analysis in arabic

no code implementations24 May 2022 Ahmed Nassar, Ebru Sezer

According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language.

Arabic Sentiment Analysis

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

Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm

1 code implementation27 Jul 2021 Mark Koren, Ahmed Nassar, Mykel J. Kochenderfer

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios.

Autonomous Vehicles reinforcement-learning +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

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