activity
20072026
most citedDifferent goals in multiscale simulations and how to reach them

37 citations · 53 across the 10 of their papers we have counts for

collaborators

14 papers

cs.CV2026

Towards Hierarchical Structure Understanding of Newspaper Images

William Mocaër, Solène Tarride, Thomas Constum +7

Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts. In this paper, we explore two compl…

cs.CV2026

Scaling State-Space Models from Lines to Paragraphs: An Ablation of Mamba-based OCR

Merveilles Agbeti-Messan, Pierrick Tranouez, Stéphane Nicolas +2

End-to-end OCR increasingly relies on autoregressive sequence models, where the quadratic cost of Transformer attention limits efficient transcription of long, paragraph-level text…

cs.CV2026

A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR

Merveilles Agbeti-Messan, Pierrick Tranouez, Stéphane Nicolas +2

End-to-end OCR for historical newspapers remains challenging, as models must handle long text sequences, degraded print quality, and complex layouts. While Transformer-based recogn…

cs.CV2026

Few-shot Writer Adaptation via Multimodal In-Context Learning

Tom Simon, Stephane Nicolas, Pierrick Tranouez +2

While state-of-the-art Handwritten Text Recognition (HTR) models perform well on standard benchmarks, they frequently struggle with writers exhibiting highly specific styles that a…

cs.CV2025

Classifying the Unknown: In-Context Learning for Open-Vocabulary Text and Symbol Recognition

Tom Simon, William Mocaer, Pierrick Tranouez +2

We introduce Rosetta, a multimodal model that leverages Multimodal In-Context Learning (MICL) to classify sequences of novel script patterns in documents by leveraging minimal exam…

cs.AI2024★ 9 cited

DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents

Thomas Constum, Pierrick Tranouez, Thierry Paquet

Information extraction from handwritten documents involves traditionally three distinct steps: Document Layout Analysis, Handwritten Text Recognition, and Named Entity Recognition.…