---
license: apache-2.0
tags:
- object-detection
- document-layout-analysis
- historical-documents
- layoutparser
- detectron2
- mask-rcnn
language:
- sv
pipeline_tag: object-detection
base_model:
- layoutparser/detectron2
---

# Historical Document Layout Detection Model

A fine-tuned Mask R-CNN model (via LayoutParser/Detectron2) for detecting layout elements in historical Swedish medical journal pages. This is a lighter model and the more complex model can be found here [Swemper-layout](https://huggingface.co/cdhu-uu/SweMPer-layout).

This model was developed as part of the research project:  
**Communicating Medicine (SweMPer): Digitalisation of Swedish Medical Periodicals, 1781–2011**  
(Project ID: **IN22-0017**), funded by **Riksbankens Jubileumsfond**.

## Project page:  
https://www.uu.se/en/department/history-of-science-and-ideas/research/research-projects-and-programmes/communicating-medicine-swemper

## Model Details

- **Model type:** Mask R-CNN (ResNet backbone)  
- **Framework:** Detectron2 / LayoutParser  
- **Fine-tuned for:** Historical document layout analysis  
- **Language of source documents:** Swedish  

## Label Map

| ID | Label            |
|----|------------------|
| 0  | Advertisement    |
| 1  | Author           |
| 2  | Header or Footer |
| 3  | Image            |
| 4  | List             |
| 5  | Page Number      |
| 6  | Table            |
| 7  | Text             |
| 8  | Title            |

## Evaluation Metrics
The evaluation metrics for this model are as follows:
|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 64.325 | 88.948 | 69.214 | 40.350 | 55.117 | 67.543 |

## Usage

### Installation

Follow instructions at:  
https://detectron2.readthedocs.io/en/latest/tutorials/install.html

### Finetuning

Follow instructions at:  
https://detectron2.readthedocs.io/en/latest/tutorials/training.html

### Inference

```python
import cv2
import layoutparser as lp
import matplotlib.pyplot as plt

# Configuration
model_config_path = "config_mask_rcnn_resized.yaml"
model_path = "SweMPer-layout-lite.pth"

label_map = {
    0: "advertisement",
    1: "author",
    2: "header_or_footer",
    3: "image",
    4: "list",
    5: "page_no",
    6: "table",
    7: "text",
    8: "title",
}

# Load model
model = lp.models.Detectron2LayoutModel(
    config_path=model_config_path,
    model_path=model_path,
    extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.8],
    label_map=label_map,
)

# Load and process image
image = cv2.imread("<path_to_image>")
image = image[..., ::-1]  # BGR to RGB

# Detect layout
layout = model.detect(image)

# Print detected elements
for block in layout:
    print(f"Type: {block.type}, Score: {block.score:.3f}, Box: {block.coordinates}")

# Visualize results
viz = lp.draw_box(image, layout, box_width=3, show_element_type=True)
plt.figure(figsize=(12, 16))
plt.imshow(viz)
plt.axis("off")
plt.show()
```

## Acknowledgements

This work was carried out within the project:  
**Communicating Medicine (SweMPer): Digitalisation of Swedish Medical Periodicals, 1781–2011**  
(Project ID: **IN22-0017**), funded by **Riksbankens Jubileumsfond**.

We gratefully acknowledge the support of the funder and project collaborators.

This model builds upon the excellent work of:

- [Detectron2](https://github.com/facebookresearch/detectron2/tree/main)  
- [LayoutParser](https://github.com/Layout-Parser/layout-parser?tab=readme-ov-file)  

We thank the contributors and maintainers of these projects for making their tools publicly available and supporting research.