---
language:
- ar
pipeline_tag: image-to-image
tags:
- arabic
- manuscripts
- historical-documents
- document-restoration
- image-restoration
- unet
- pytorch
datasets:
- Archatext/AraMS-28k-HTR
- Archatext/AraMS-Restore
---

# AraMS-Restore — restoration generators

Two U-Net generators that take a **degraded Arabic manuscript line** and return a
readable one. Same architecture, same data, same schedule — they differ only in
what they optimize:

| | `unimodal` (Level 1) | `recaware` (Level 2) |
|---|---|---|
| Loss | L1 + ResNet50 perceptual | the same **+ a frozen HATFormer OCR critic** |
| Optimizes for | visual fidelity | **legibility** |
| Text | not used | used as a loss target, never as an input |

The Level-2 critic reads the restored image and is scored against the true
transcription, so gradients flow into the pixels that matter for reading. Code:
[github.com/ArchaText/AraMS-Restore](https://github.com/ArchaText/AraMS-Restore).

## Results

On **real** damaged lines from a held-out manuscript — the honest test — the
image-only model does not help, while the recognition-aware one does:

| Input to the reader | CER % | WER % |
|---|---|---|
| Degraded (untouched) | 29.81 | 59.98 |
| Level 1 restored | 30.15 | 59.98 |
| **Level 2 restored** | **28.15** | **58.44** |

The ranking *inverts* between the two ways of measuring — Level 1 wins every pixel
metric it is optimized for, and loses on reading:

| | PSNR ↑ | SSIM ↑ | CER ↓ |
|---|---|---|---|
| Degraded | 22.76 | 0.9498 | 35.11 |
| Level 1 | **34.66** | **0.9886** | 28.12 |
| Level 2 | 30.66 | 0.9781 | **27.13** |

*(Synthetic test set. CER read by the frozen HATFormer reader.)*

## Files

```
unimodal/best.pt      124 MB   Level 1 generator (plain state_dict)
recaware/best.pt      124 MB   Level 2 generator (plain state_dict)
```

Each is a `state_dict` for the generator built by
`restoration.models.unet_pix2pix.build_generator`: 31.04 M parameters, float32,
RGB in/out, `base=64`, `depth=4`, no discriminator (pix2pix off).

## Usage

```bash
hf download Archatext/Restoration --local-dir checkpoints/restore
```

That is exactly the layout the configs resolve, so the repo's scripts work
unchanged:

```bash
python scripts/restore_real.py --score        # CER before vs. after, both models
python app/server.py                          # drag-and-drop web demo
```

Or load one directly:

```python
import torch, yaml
from restoration.models.unet_pix2pix import build_generator

cfg = yaml.safe_load(open("configs/restore_recaware.yaml"))
gen = build_generator(cfg["model"])
gen.load_state_dict(torch.load("checkpoints/restore/recaware/best.pt", map_location="cpu"))
gen.eval()
```

### Input representation

A line is resized to height 64 keeping aspect and right-padded to width 1152
(`build_strip`), RGB in [0, 1], padding pure black and excluded by a mask. 1152 =
3 × 384 tiles cleanly into the HATFormer 384 × 384 RTL-flipped canvas, which is
what makes the Level-2 recognition loss exact. Feeding a differently-shaped or
grayscale image will degrade output quality.

## Training

Both models: 20 epochs, AdamW `lr=2e-4` (β=0.5, 0.999), batch 8, seed 1337, and 4
distinct degraded variants per clean line per epoch. Damage is synthesized **on the
fly** from clean `train`-split lines only — never val/test — by the localized
degradation engine (erasure, tears, worm holes, bleed-through, ink blots, ink
feathering), calibrated against real manuscript decay.

| | weights |
|---|---|
| `unimodal` | L1 1.0, perceptual 0.02 |
| `recaware` | image 1.0 (L1 1.0 + perceptual 0.02), recognition 0.5 |

Level 2's critic is [`Archatext/hatformer-arams28k`](https://huggingface.co/Archatext/hatformer-arams28k),
frozen throughout.

Training data: [AraMS-28k-HTR](https://huggingface.co/datasets/Archatext/AraMS-28k-HTR),
`train` split — 20,103 lines from 9 manuscripts.

## Leakage

Splits are manuscript-level disjoint: the evaluation manuscripts (book_03, 05, 09)
are held out of restoration training entirely, **and** the OCR critic that scores
the output never trained on them either. Both conditions are needed — a critic that
had seen the test books would contaminate the training signal and the metric at
once.

## Limitations

- Trained on **synthetic** degradation; real decay is a different distribution, and
  the gap shows — the CER gain on real damage (1.7 points) is smaller than on the
  synthetic test set.
- Single script tradition and a single corpus of 14 manuscripts; no claim of
  generalization to other hands, papers or languages.
- Level 1 makes real damage marginally *harder* to read (30.15 vs 29.81 CER) while
  scoring far better on PSNR/SSIM — do not select a restoration model on pixel
  metrics alone.
- Line-level only. Page segmentation is upstream and not part of this release.

## Citation

<!-- TODO: thesis / paper reference -->