impresso-project/frakturline-dataset

---
language:
- de
- fr
- lb
license: agpl-3.0
tags:
- image-classification
- pytorch
- fraktur
- other
- historical-documents
- ocr
- impresso
datasets:
- impresso-project/frakturline-dataset
- impresso-project/frakturline-testset
pipeline_tag: image-classification
---

# Fraktur/Other Text-Line Classifier

A binary CNN classifier that determines whether a scanned text-line image is
set in **Fraktur** (blackletter / Gothic script) or **Other** (primarily Latin /
Roman / Antiqua script).

Developed for the [Impresso](https://impresso-project.ch/) digital humanities
project, which processes millions of historical newspaper pages in German,
French, Luxembourgish, and other European languages.

---

## Model Details

| Property | Value |
| ------------- | -------------------------------------------------------------------------- |
| Architecture | `BinaryClassificationCNN` — 3-layer CNN with LayerNorm and Dropout |
| Input | Grayscale text-line image, resized/padded to **60 × 800 px** |
| Output | Single logit; `logit > 0` → Fraktur (equivalent to `sigmoid(logit) > 0.5`) |
| Parameters | ~2.1 M |
| Training data | ~32 000 manually labeled line crops from Swiss/Luxembourgish newspapers |
| Framework | PyTorch |

### Architecture

```
Input (1, 60, 800)
→ Conv2d(1→32) + ReLU + MaxPool2d → LayerNorm[32, 30, 400]
→ Conv2d(32→64) + ReLU + MaxPool2d → LayerNorm[64, 15, 200] + Dropout(0.15)
→ Conv2d(64→128) + LayerNorm[128,15,200] + ReLU + AdaptiveMaxPool2d(1×8)
→ Flatten(1 024) → FC(128) + ReLU → FC(1)
```

### Training

- **Loss**: `BCEWithLogitsLoss`
- **Optimizer**: Adam, lr = 1e-4 with `ReduceLROnPlateau` (factor 0.5, patience 2)
- **Epochs**: up to 20 with early stopping (patience 5)
- **Augmentation**: random rotation ±2°, Gaussian noise (σ=0.05), random right-masking (p=0.15, up to 50 % of width) to improve robustness on short lines
- **Class balancing**: `WeightedRandomSampler` (other ≈ 20 k, fraktur ≈ 14 k)

---

## Performance

Evaluated on the companion held-out test set ([impresso-project/frakturline-testset](https://huggingface.co/datasets/impresso-project/frakturline-testset)) — 2 000 balanced images (1 000 per class), strictly excluded from training:

| Metric | Score |
| ------------------- | ----------- |
| Accuracy | **99.75 %** |
| Precision (Fraktur) | **100.0 %** |
| Recall (Fraktur) | **99.5 %** |
| F1 (Fraktur) | **99.75 %** |
| FP / FN | 0 FP / 5 FN |

---

## Evaluation Dataset

The test set is published as a separate frozen HF dataset:

→ [impresso-project/frakturline-testset](https://huggingface.co/datasets/impresso-project/frakturline-testset)

It is **not** included in the training corpus. Please consult the dataset card for detailed provenance, revision, and licensing information. Do not use it for training.

```python
from datasets import load_dataset
ds = load_dataset("impresso-project/frakturline-testset", split="test")
# 2 000 images: {"image": <PIL.Image>, "label": "fraktur"|"other", ...}
```

---

## Usage

### Install dependencies

```bash
pip install torch torchvision Pillow huggingface_hub
```

### Classify images

```python
from huggingface_hub import hf_hub_download
import importlib.util

# Load pipeline.py from the hub
spec = importlib.util.spec_from_file_location(
"pipeline",
hf_hub_download("impresso-project/frakturline-classification-cnn", "pipeline.py"),
)
pipeline_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(pipeline_module)

pipe = pipeline_module.FrakturPipeline.from_pretrained(
"impresso-project/frakturline-classification-cnn"
)

# Single image — local path
result = pipe("path/to/line.png")
# → {"label": "fraktur", "score": 0.9731}

# Single image — URL
result = pipe("https://example.com/line.png")

# Batch
results = pipe(["line1.png", "line2.png", "line3.png"])
```

### Input format

- Any PIL-readable image format (PNG, JPEG, TIFF, …)
- Ideally a single text line crop extracted by an OCR layout-analysis tool
- The pipeline handles grayscale conversion and resizing internally

### Output format

```python
{"label": "fraktur", "score": 0.9731} # sigmoid probability of predicted class
{"label": "other", "score": 0.9954}
```

---

## Limitations

- Designed for **single text lines**. Mixed-typeface lines or non-text content may produce unreliable results.
- Short headers, ornaments, or lines with very few characters can be ambiguous.
- The training data is drawn primarily from 19th- and 20th-century European newspapers; performance on other periods or regions is not guaranteed.

---

## Citation

If you use this model, please cite the Impresso project:

```bibtex
@misc{impresso2025fraktur,
title = {Fraktur/Antiqua Text-Line Classifier},
author = {Impresso Project},
year = {2025},
url = {https://huggingface.co/impresso-project/frakturline-classification-cnn}
}
```

---

## License

The code in this repository is released under the **GNU Affero General Public License v3.0 (AGPL-3.0)**.

The model was trained on data derived from multiple upstream sources. Rights in the underlying source materials remain subject to their respective original terms. For dataset-specific provenance, revision, and licensing details, please consult the linked dataset cards.

If you use this model, please cite the [Impresso project](https://impresso-project.ch/) and link to this repository.