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---
language:
- ko
tags:
- ocr
widget:
- src: https://raw.githubusercontent.com/ddobokki/ocr_img_example/master/g.jpg
example_title: word1
- src: https://raw.githubusercontent.com/ddobokki/ocr_img_example/master/khs.jpg
example_title: word2
- src: https://raw.githubusercontent.com/ddobokki/ocr_img_example/master/m.jpg
example_title: word3
pipeline_tag: image-to-text
---
# korean trocr model
## train datasets
AI Hub
- [๋ค์ํ ํํ์ ํ๊ธ ๋ฌธ์ OCR](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=91)
- [๊ณต๊ณตํ์ ๋ฌธ์ OCR](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=88)
## model structure
- encoder : [trocr-base-stage1's encoder](https://huggingface.co/microsoft/trocr-base-stage1)
- decoder : [KR-BERT-char16424](https://huggingface.co/snunlp/KR-BERT-char16424)
## how to use
```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer
import requests
import unicodedata
from io import BytesIO
from PIL import Image
processor = TrOCRProcessor.from_pretrained("ddobokki/ko-trocr")
model = VisionEncoderDecoderModel.from_pretrained("ddobokki/ko-trocr")
tokenizer = AutoTokenizer.from_pretrained("ddobokki/ko-trocr")
url = "https://raw.githubusercontent.com/ddobokki/ocr_img_example/master/g.jpg"
response = requests.get(url)
img = Image.open(BytesIO(response.content))
pixel_values = processor(img, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values, max_length=64)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
generated_text = unicodedata.normalize("NFC", generated_text)
print(generated_text)
``` |