3 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…