metadata
tags:
- trocr
- image-to-text
widget:
- src: >-
https://huggingface.co/dsupa/mangaocr-hoogberta-v1/to_test_hf_model/test1.JPG
example_title: test 1
- src: >-
https://huggingface.co/dsupa/mangaocr-hoogberta-v1/to_test_hf_model/test2.JPG
example_title: test 2
- src: >-
https://huggingface.co/dsupa/mangaocr-hoogberta-v1/to_test_hf_model/test3.JPG
example_title: test 3
- src: >-
https://huggingface.co/dsupa/mangaocr-hoogberta-v1/to_test_hf_model/test4.JPG
example_title: test 4
- src: >-
https://huggingface.co/dsupa/mangaocr-hoogberta-v1/to_test_hf_model/test5.JPG
example_title: test 5
- src: >-
https://huggingface.co/dsupa/mangaocr-hoogberta-v1/to_test_hf_model/test6.JPG
example_title: test 6
How to use
Here is how to use this model in PyTorch:
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
processor = TrOCRProcessor.from_pretrained('dsupa/mangaocr-hoogberta-v2')
model = VisionEncoderDecoderModel.from_pretrained('dsupa/mangaocr-hoogberta-v2')
def predict(image_path):
image = Image.open(image_path).convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
image_path = "your_img.jpg"
pred = predit(image_path)
print(pred)