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---
license: apache-2.0
datasets:
- Riksarkivet/goteborgs_poliskammare_fore_1900_lines
- Riksarkivet/bergskollegium_relationer_och_skrivelser_lines
- Riksarkivet/bergskollegium_advokatfiskalskontoret_seg
- Riksarkivet/frihetstidens_utskottshandlingar
- Riksarkivet/frihetstidens_utskottshandlingar_seg
- Riksarkivet/gota_hovratt_seg
- Riksarkivet/jonkopings_radhusratts_och_magistrat_seg
- Riksarkivet/krigshovrattens_dombocker_seg
- Riksarkivet/svea_hovratt_seg
- Riksarkivet/trolldomskommissionen_seg
pipeline_tag: image-segmentation
tags:
- text-line-detection
- text-region-detection
- document-analysis
- historical-documents
- handwritten-text
- rf-detr
- instance-segmentation
---
# RF-DETR Seg-Preview: Historical Document Instance Segmentation
This model is trained to detect and segment text lines and text regions from historical handwritten documents spanning from the 16th to the 20th century.
## Model Description
RF-DETR Seg-Preview is an instance segmentation model based on the RF-DETR architecture. It predicts:
- Bounding boxes for text elements
- Class labels (text_region or text_line)
- Instance segmentation masks
### Classes
The model detects two classes:
- **text_region** (index: 1) - Larger regions of text content
- **text_line** (index: 2) - Individual lines of text
## Training Data
The model was trained on historical handwritten documents with the following data distribution:
- **Training set**: 11,495 images
- **Validation set**: 2,711 images
- **Test set**: 2,340 images
## Performance Metrics
### Validation Set Performance
| Class | mAP@50:95 | mAP@50 | Precision | Recall |
|-------|-----------|--------|-----------|--------|
| text_region | 0.822 | 0.963 | 0.949 | 0.940 |
| text_line | 0.621 | 0.936 | 0.957 | 0.940 |
| **Overall** | **0.721** | **0.950** | **0.953** | **0.940** |
### Test Set Performance
| Class | mAP@50:95 | mAP@50 | Precision | Recall |
|-------|-----------|--------|-----------|--------|
| text_region | 0.822 | 0.959 | 0.949 | 0.940 |
| text_line | 0.688 | 0.955 | 0.978 | 0.940 |
| **Overall** | **0.755** | **0.957** | **0.964** | **0.940** |
## Training Metrics
## Use Cases
This model is particularly suitable for:
- Text line detection for OCR preprocessing
- Document digitization projects involving historical manuscripts
- Historical document understanding and analysis
## Limitations
- The model is specifically trained on historical handwritten documents (16th-20th century)
- Performance may vary on modern printed documents or documents outside the training distribution
- Performance depends on image quality and document preservation state
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