Scaling State-Space Models from Lines to Paragraphs: An Ablation of Mamba-based OCR
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. State-Space Models (SSMs) such as Mamba offer linear-time decoding and have recently been shown to match Transformer accuracy on printed historical lines, but their behavior as sequences grow from short lines to full paragraphs, and their generalization to handwriting, remain poorly understood. We study how a Mamba-based OCR recognizer scales from lines to paragraphs. We first conduct a systematic exploration of its four core hyperparameters (decoder depth, state dimension, expansion factor, and connector dept...
| Authors | Merveilles Agbeti-Messan, Pierrick Tranouez, Stéphane Nicolas et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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