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import os |
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os.environ['USE_TORCH'] = '1' |
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from doctr.io import DocumentFile |
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from doctr.models import ocr_predictor |
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import gradio as gr |
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from PIL import Image |
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import base64 |
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from utils import HocrParser |
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predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', reco_arch='crnn_vgg16_bn',pretrained=True) |
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title="DocTR OCR (PDL Demo)" |
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description="Upload an image to get the OCR results !" |
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def greet(img): |
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img.save("out.jpg") |
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doc = DocumentFile.from_images("out.jpg") |
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output=predictor(doc) |
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xml_outputs = output.export_as_xml() |
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parser = HocrParser() |
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res="" |
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for obj in output.pages: |
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for obj1 in obj.blocks: |
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for obj2 in obj1.lines: |
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for obj3 in obj2.words: |
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res=res + " " + obj3.value |
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res=res + "\n" |
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res=res + "\n" |
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_output_name = "RESULT_OCR.txt" |
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_output_name_pdf="RESULT_OCR.pdf" |
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open(_output_name, 'w').close() |
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with open(_output_name, "w", encoding="utf-8", errors="ignore") as f: |
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f.write(res) |
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print("Writing into file") |
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base64_encoded_pdfs = list() |
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for i, (xml, img) in enumerate(zip(xml_outputs, doc)): |
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xml_element_tree = xml[1] |
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parser.export_pdfa(_output_name_pdf, |
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hocr=xml_element_tree, image=img) |
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with open(_output_name_pdf, 'rb') as f: |
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base64_encoded_pdfs.append(base64.b64encode(f.read())) |
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return res, _output_name, _output_name_pdf |
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demo = gr.Interface(fn=greet, |
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inputs=gr.Image(type="pil"), |
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outputs=["text", "file","file"], |
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title=title, |
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description=description, |
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examples=[["Examples/Book.png"],["Examples/News.png"],["Examples/Manuscript.jpg"],["Examples/Files.jpg"]] |
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) |
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demo.launch(debug=True) |
