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app.py
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@@ -80,7 +80,7 @@ def process_image(image):
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title = "Extracting Receipts: LayoutLMv3"
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description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.08387' target='_blank'>LayoutLMv3: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>"
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examples =[['test0.jpeg'],['test1.jpeg'],['test2.jpeg']]
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title = "Extracting Receipts: LayoutLMv3"
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description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. <br>This particular model is fine-tuned from [LayoutLMv3](https://huggingface.co/microsoft/layoutlmv3-base) on Consolidated Receipt Dataset [CORD](https://github.com/clovaai/cord), a dataset of receipts. If you search the π€ Hugging Face hub you will see other related models fine-tuned for other documents. This model is trained using fine-tuning to look for entities around menu items, subtotal, and total prices. To perform your own fine-tuning, take a look at the [notebook by Niels](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3). <br>To try it out, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. To see the output bigger, right-click on it, select 'Open image in new tab', and use your browser's zoom feature."
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.08387' target='_blank'>LayoutLMv3: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>"
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examples =[['test0.jpeg'],['test1.jpeg'],['test2.jpeg']]
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