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arXiv Computer Vision By Sheng-Wei Chan, Yung-Che Wang, Hsin-Jui Pan, Chia-Min Lin, Jen-Shiun Chiang

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

Read the original on arXiv Computer Vision →

DeepMine-Mamba addresses information dilution in Mamba-based state space models for document image binarization. The authors show that state-space propagation amplifies both foreground and background, reducing contrast, and introduce an Anti‑Dilution Gate that uses propagation‑induced differences and a learned stroke prior to preserve stroke details. On DIBCO/H‑DIBCO benchmarks without external data, the method matches transformer and gated‑convolution baselines and improves all four evaluation metrics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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