
# korean trocr model

## train datasets
AI Hub
- [다양한 형태의 한글 문자 OCR](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=91)
- [공공행정문서 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)
```
{"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"}
transformers pytorch safetensors vision-encoder-decoder ocr image-to-text ko endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #ocr #image-to-text #ko #endpoints_compatible #region-us 


# korean trocr model

## train datasets
AI Hub
- 다양한 형태의 한글 문자 OCR
- 공공행정문서 OCR

## model structure
- encoder : trocr-base-stage1's encoder
- decoder : KR-BERT-char16424

## how to use


# korean trocr model ## train datasets
AI Hub
- 다양한 형태의 한글 문자 OCR
- 공공행정문서 OCR ## model structure
- encoder : trocr-base-stage1's encoder
- decoder : KR-BERT-char16424 ## how to use
TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #ocr #image-to-text #ko #endpoints_compatible #region-us 
 # korean trocr model ## train datasets
AI Hub
- 다양한 형태의 한글 문자 OCR
- 공공행정문서 OCR ## model structure
- encoder : trocr-base-stage1's encoder
- decoder : KR-BERT-char16424 ## how to use
TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #ocr #image-to-text #ko #endpoints_compatible #region-us 
# korean trocr model## train datasets
AI Hub
- 다양한 형태의 한글 문자 OCR
- 공공행정문서 OCR## model structure
- encoder : trocr-base-stage1's encoder
- decoder : KR-BERT-char16424## how to use