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doctr-torch-crnn_vgg16_bn-danish-v1

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doctr-torch-crnn_vgg16_bn-danish-v1
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---
language:
- en
- da
---


<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>

**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**

## Task: recognition

https://github.com/mindee/doctr

This model does a good job if you need to do OCR on Danish documents. 

### Example usage:

```python

from doctr.io import DocumentFile
from doctr.models import ocr_predictor, from_hub

reco_arch = from_hub('diversen/doctr-torch-crnn_vgg16_bn-danish-v1')
det_arch = "db_resnet50"

model = ocr_predictor(det_arch=det_arch, reco_arch=reco_arch, pretrained=True)
image = DocumentFile.from_images(['test.jpg'])

result = model(image)
result.show()

output = result.export()
text_str = ""

for block in output["pages"][0]["blocks"]:
    block_txt = ""
    for line in block["lines"]:
        line_txt = ""
        for word in line["words"]:
            line_txt += word["value"] + " "
        block_txt += line_txt + "\n"
    text_str += block_txt + "\n"

print(text_str)
```

### Run Configuration

{
  "arch": "crnn_vgg16_bn",
  "train_path": "train-data",
  "val_path": "validation-data",
  "train_samples": 1000,
  "val_samples": 20,
  "font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
  "min_chars": 1,
  "max_chars": 32,
  "name": "doctr-torch-crnn_vgg16_bn-danish-v1",
  "epochs": 1,
  "batch_size": 64,
  "device": 0,
  "input_size": 32,
  "lr": 0.001,
  "weight_decay": 0,
  "workers": 16,
  "resume": "crnn_vgg16_bn_20240317-095746.pt",
  "vocab": "danish",
  "test_only": false,
  "freeze_backbone": false,
  "show_samples": false,
  "wb": false,
  "push_to_hub": true,
  "pretrained": true,
  "sched": "cosine",
  "amp": false,
  "find_lr": false,
  "early_stop": false,
  "early_stop_epochs": 5,
  "early_stop_delta": 0.01
}