DeepMine-Mamba framework enhances document binarization with Mamba models

Researchers have introduced DeepMine-Mamba, a new framework for document image binarization that utilizes Mamba-based state space models. The proposed method addresses information dilution issues inherent in direct state-space propagation, particularly for faint or fragmented text strokes. A novel Anti-Dilution Gate is incorporated to selectively restore local responses sensitive to stroke details while mitigating background enhancement, leading to competitive performance on DIBCO/H-DIBCO benchmarks. AI

IMPACT Introduces a novel approach to document image binarization using Mamba models, potentially improving performance on degraded documents.

RANK_REASON This is a research paper introducing a novel model architecture for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• DeepMine-Mamba
• DIBCO/H-DIBCO
• Mamba

• paper
• model release

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