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Riksarkivet
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yolov9-regions-1

HTRflow
instance segmentation
text regions
handwritten
htr
Model card Files Community
yolov9-regions-1
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---
datasets:
- Riksarkivet/trolldomskommissionen_seg
- Riksarkivet/svea_hovratt_seg
- Riksarkivet/krigshovrattens_dombocker_seg
- Riksarkivet/jonkopings_radhusratts_och_magistrat_seg
- Riksarkivet/gota_hovratt_seg
- Riksarkivet/frihetstidens_utskottshandlingar_seg
- Riksarkivet/bergskollegium_relationer_och_skrivelser_seg
- Riksarkivet/bergskollegium_advokatfiskalskontoret_seg
tags:
- instance segmentation
- text regions
- handwritten
- htr

library_name: htrflow

---

# Yolov9-textregions-handwritten

<!-- Provide a quick summary of what the model is/does. -->

A yolov9 instance segmentation model for segmenting text-regions in handwritten running-text documents


## Model Details

### Model Description

This model was developed for segmenting text-regions in handwritten running-text documents. It is meant to be implemented in an HTR-pipeline
where one first segment text-regions, then text-lines within the regions, and then feed these text-lines to an HTR-model.



- **Developed by:** The Swedish National Archives
- **Model type:** yolov9
- **License:** [More Information Needed]

### Model Sources [optional]


- **Repository:** [yolov9-regions-1](https://huggingface.co/Riksarkivet/yolov9-regions-1)
- **Paper:** [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)

## Uses


### Direct Use

Segment text-regions in handwritten running-text documents

### Downstream Use [optional]

As part of an HTR-pipeline for transcribing entire pages of handwritten running-text documents. See [Swedish Lion Libre](https://huggingface.co/Riksarkivet/trocr-base-handwritten-hist-swe-2)
for example usage with the [HTRflow package](https://github.com/AI-Riksarkivet/htrflow)


## How to Get Started with the Model

### How to Load and Use the YOLOv9 Instance Segmentation Model

Below is the Python code to load and use the trained YOLOv9 instance segmentation model using the Ultralytics repo:

```python
import torch
from ultralytics import YOLO

# Load the trained YOLOv9 model
model = YOLO('path/to/your/model.pt')  # Update with the correct path to your trained model

# Load an image
img = 'path/to/your/image.jpg'  # Update with the path to the image you want to use

# Perform instance segmentation
results = model(img)

# Display results
results.show()  # Show image with predicted masks

# To get the raw predictions (bounding boxes, masks, etc.)
for result in results:
    print(result.boxes)  # Bounding boxes
    print(result.masks)  # Segmentation masks
```

### Usage with the HTRflow package

See the model card for [Swedish Lion Libre](https://huggingface.co/Riksarkivet/trocr-base-handwritten-hist-swe-2)
for example usage with the [HTRflow package](https://github.com/AI-Riksarkivet/htrflow), or refer to the documentation for 
[HTRflow](https://github.com/AI-Riksarkivet/htrflow)

## Training Details

### Training Data

[Trolldomskommissionen](https://huggingface.co/datasets/Riksarkivet/trolldomskommissionen_seg)  
[Svea Hovrätt](https://huggingface.co/datasets/Riksarkivet/svea_hovratt_seg)  
[Krigshovrättens domböcker](https://huggingface.co/datasets/Riksarkivet/krigshovrattens_dombocker_seg)  
[Jönköpings rådhusrätt och magistrat](https://huggingface.co/datasets/Riksarkivet/jonkopings_radhusratts_och_magistrat_seg)  
[Göta hovrätt](https://huggingface.co/datasets/Riksarkivet/gota_hovratt_seg)  
[Frihetstidens utskottshandlingar](https://huggingface.co/datasets/Riksarkivet/frihetstidens_utskottshandlingar_seg)  
[Bergskollegium relationer och skrivelser](https://huggingface.co/datasets/Riksarkivet/bergskollegium_relationer_och_skrivelser_seg)  
[Bergskollegium advokatfiskalkontoret](https://huggingface.co/datasets/Riksarkivet/bergskollegium_advokatfiskalskontoret_seg)  



### Training Procedure


#### Training Hyperparameters

See [training config](https://huggingface.co/Riksarkivet/yolov9-regions-1/blob/main/args.yaml) at model repo


## Evaluation

See [training results](https://huggingface.co/Riksarkivet/yolov9-regions-1/blob/main/results.csv)


#### Metrics

Standard metrics for instance segmentation. Note that evaluation of segmentation as part of an HTR-pipeline should be measured by what effect it
has on the following HTR, that is, CER and WER. For implementation and evaluation of entire HTR-pipelines, please check out [HTRflow](https://github.com/AI-Riksarkivet/htrflow),
the Swedish National Archive's open-source package for HTR and OCR projects.


### Model Architecture and Objective

yolov9

#### Software

[Ultralytics](https://github.com/ultralytics)

## Citation [optional]

[YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)