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arXiv · 2606.08781

DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization

Sheng-Wei Chan·Yung-Che Wang·Hsin-Jui Pan·Chia-Min Lin·Jen-Shiun Chiang
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Abstract

Document image binarization aims to separate foreground text from degraded backgrounds while preserving thin, broken, and low-contrast strokes. Although deep learning methods have improved binarization performance, most existing approaches rely on convolutional, transformer-based, or generative architectures, while Mamba-based state space models remain largely unexplored for this task. In this work, we investigate Mamba-based feature propagation and observe that direct state-space propagation may dilute weak foreground cues during long-range modeling, especially faint ink traces, fragmented characters, and boundary-sensitive stroke details. To address this problem, we propose DeepMine-Mamba, a Mamba-based binarization framework equipped with a novel Anti-Dilution Gate that estimates propagation-induced feature changes and selectively restores stroke-sensitive local responses while suppressing unnecessary background enhancement. Experiments on DIBCO/H-DIBCO benchmarks under a strict leave-one-year-out protocol show that DeepMine-Mamba achieves competitive overall performance, with strong average FM and Fps across benchmark years. Ablation results further show that the Anti-Dilution Gate is the key component for mitigating propagation-induced foreground dilution and improving stroke preservation.

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Sheng-Wei Chan, Yung-Che Wang, Hsin-Jui Pan, Chia-Min Lin, Jen-Shiun Chiang. 2026-06-07. DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization. https://arxiv.org/abs/2606.08781

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