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Muscrat Ink Sep

Manuscript Ink Layer Separation — U-Net (ResNet-34)

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280 MB
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vRevision e59e384
Updated
Oct 8, 2026
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Hugging Face

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Manuscript Ink Layer Separation — U-Net (ResNet-34)

A binary segmentation model for separating ink from parchment in medieval manuscript fragment images. Given a high-resolution manuscript scan, the model produces an ink mask, an isolated ink layer (white background), and a parchment layer (ink removed).

Architecture: U-Net with ResNet-34 encoder, trained from scratch (no pretrained weights), 2-class output (background / ink).

Inference: Sliding window over full-resolution images (default 512 px window, 256 px stride) with averaged overlapping predictions and morphological post-processing to reduce halo artifacts.

Trained on: an annotated collection of medieval liturgical musical manuscripts, vernacular Icelandic saga manuscripts, and fragments featuring various parchment qualities.

Validation IoU: .767

Intended use: Research tool for manuscript studies and digital humanities. Not intended for printed documents or modern text.

Status: currently one version for ink separation, and so should only be considered as a piece of a work in progress.

Running guideline: if you're getting some odd behavior, widen the window size to 900 and decrease confidence.

Sources

1 source
Hugging FacePrimaryVersion e59e38425cfaaa9b64fad053a5159746293da3d1
huggingface.co/grackle-in-an-HEB-parking-lot/muscrat-ink-sep/tree/e59e38425cfaaa9b64fad053a5159746293da3d1

Verified Aug 25

Model artifacts

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best_model_14april.pth

pth · 280 MB · SHA-256 a676b5522397…1070 · Hugging Face

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Source excerpts

2 excerpts

--- license: mit pipeline_tag: image-segmentation tags: - version2 --- Manuscript Ink Layer Separation — U-Net (ResNet-34) A binary segmentation model for separating ink from parchment in medieval manuscript fragment images. Given a high-resolution manuscript scan, the model produces an ink mask, an isolated ink layer (white background), and a parchment layer (ink removed). Architecture: U-Net with ResNet-34 encoder, trained from scratch (no pretrained weights), 2-class output (background / ink). Inference: Sliding window over full-resolution images (default 512 px window, 256 px stride) with averaged overlapping predictions and morphological post-processing to reduce halo artifacts. Trained on: an annotated collection of medieval liturgical musical manuscripts, vernacular Icelandic saga manuscripts, and fragments featuring various parchment qualities. Validation IoU: .767 Intended use: Research tool for manuscript studies and digital humanities. Not intended for printed documents or modern text. Status: currently one version for ink separation, and so should only be considered as a piece of a work in progress. Running guideline: if you're getting some odd behavior, widen the window size to 900 and decrease confidence.

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo grackle-in-an-HEB-parking-lot/muscrat-ink-sep