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ddobokki
/
ko-trocr

Image-to-Text
Transformers
PyTorch
Safetensors
Korean
vision-encoder-decoder
image-text-to-text
ocr
Inference Endpoints
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ko-trocr / README.md
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metadata
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

model structure

how to use 

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)