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import os | |
os.environ['USE_TORCH'] = '1' | |
from doctr.io import DocumentFile | |
from doctr.models import ocr_predictor | |
import gradio as gr | |
from PIL import Image | |
predictor = ocr_predictor(pretrained=True) | |
title="DocTR OCR (PDL Demo)" | |
description="Upload an image to get the OCR results !" | |
def greet(img): | |
img.save("out.jpg") | |
doc = DocumentFile.from_images("out.jpg") | |
output=predictor(doc) | |
res="" | |
for obj in output.pages: | |
for obj1 in obj.blocks: | |
for obj2 in obj1.lines: | |
for obj3 in obj2.words: | |
res=res + " " + obj3.value | |
res=res + "\n" | |
res=res + "\n" | |
_output_name = "RESULT_OCR.txt" | |
open(_output_name, 'w').close() # clear file | |
with open(_output_name, "w", encoding="utf-8", errors="ignore") as f: | |
f.write(res) | |
print("Writing into file") | |
return res, _output_name | |
demo = gr.Interface(fn=greet, | |
inputs=gr.Image(type="pil"), | |
outputs=["text", "file"], | |
title=title, | |
description=description | |
examples=[ | |
["Examples\Book.png"], | |
["Examples\News.png"], | |
["Examples\Manuscript.jpg"], | |
["Examples\Files.jpg"], | |
], | |
) | |
demo.launch(debug=True) |