---
license: mit
library_name: segmentation-models-pytorch
pipeline_tag: image-segmentation
tags:
  - comics
  - manga
  - text-segmentation
language:
  - en
  - ja
  - ko
  - zh
---

# comic-text-mask

Binary **text-mask segmenter** for the
[comic-localizer](https://github.com/TareHimself/comic-localizer) cleaning
pipeline. It runs on a detector's text-region crop and returns a per-pixel
"text vs not-text" mask that feeds LaMa inpainting. Locating and grouping text
is the detector's job, not this model's.

Training data (see the [training repo](https://github.com/TareHimself/comic-localizer-text-masking)): synthetic text rendered onto
(a) procedurally generated flat surfaces with synthetic clutter and (b) real
cleaned comic pages, framed as detector-style crops. Text is Latin, Japanese
(kana + kanji, horizontal and vertical), Korean, and Chinese, with a large
fraction of random-glyph runs so rare characters are covered. No source imagery
is redistributed.

Validation (held-out synthetic + real crops): IoU 0.906,
precision 0.949, recall 0.952.

## Use

```python
import json, torch
from huggingface_hub import hf_hub_download

meta = json.load(open(hf_hub_download("TareHimself/comic-text-mask", "tm_meta.json")))
model = torch.jit.load(hf_hub_download("TareHimself/comic-text-mask", "model.pt")).eval()
S = meta["imgsz"]

# letterbox `rgb` (H,W,3 uint8) into an SxS square, pad 0, keep the paste box
# ... then:
x = torch.from_numpy(square).permute(2, 0, 1).unsqueeze(0)      # (1,3,S,S) uint8
prob = model(x)[0, 0].numpy()                                   # (S,S) float
mask = (prob > meta["threshold"]).astype("uint8") * 255
# crop the paste box back out and resize to the original size
```

Or load the raw weights with `segmentation-models-pytorch`:

```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("TareHimself/comic-text-mask")   # normalisation NOT baked in
```

`model.pt` has `/255`, ImageNet normalisation, and the final sigmoid baked into
the graph; it expects letterboxed **uint8** RGB. `model.safetensors` is the
pristine network.