|
--- |
|
library_name: transformers |
|
license: cc-by-4.0 |
|
language: |
|
- 'no' |
|
- 'nb' |
|
- 'nn' |
|
base_model: |
|
- microsoft/trocr-base-handwritten |
|
--- |
|
|
|
# Model Card for Sprakbanken/TrOCR-norhand-v3 |
|
This is a TrOCR-model for OCR (optical character recognition) of handwritten historic documents written in Norwegian. |
|
It can be used to recognize text in images of handwritten text. |
|
|
|
## How to Get Started with the Model |
|
|
|
Use the code below to get started with the model. |
|
|
|
```python |
|
from transformers import TrOCRProcessor, VisionEncoderDecoderModel |
|
from PIL import Image |
|
|
|
processor = TrOCRProcessor.from_pretrained("Sprakbanken/TrOCR-norhand-v3") |
|
model = VisionEncoderDecoderModel.from_pretrained("Sprakbanken/TrOCR-norhand-v3") |
|
|
|
image = Image.open("path_to_image.jpg").convert("RGB") |
|
|
|
pixel_values = processor(image, return_tensors="pt").pixel_values |
|
generated_ids = model.generate(pixel_values) |
|
|
|
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
|
``` |
|
|
|
## Model Details |
|
This model is [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) fine-tuned on the [Huggingface version](https://huggingface.co/datasets/Teklia/NorHand-v3-line) of the [NorHand v3 dataset](https://zenodo.org/records/10255840). |
|
|
|
### Model Description |
|
|
|
- **Developed by:** The National Library of Norway |
|
- **Model type:** TrOCR |
|
- **Languages:** Norwegian (mostly >100 years old) |
|
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
|
- **Finetuned from model :** [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-handwritten) |
|
|
|
## Uses |
|
You can use the raw model for handwritten text recognition (HTR) on single text-line images in Norwegian. |
|
|
|
### Out-of-Scope Use |
|
The model only works with images of lines of text. |
|
If you have images of entire pages of text, you must segment the text into lines first to benefit from this model. |
|
|
