Instructions to use seungwon12/layoutlm-document-extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seungwon12/layoutlm-document-extract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="seungwon12/layoutlm-document-extract")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("seungwon12/layoutlm-document-extract") model = AutoModelForTokenClassification.from_pretrained("seungwon12/layoutlm-document-extract") - Notebooks
- Google Colab
- Kaggle
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Create README.md
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README.md
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---
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language:
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- ko
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- en
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---
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# Document extract
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## This model is layoutlmv2 base model
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> if you want to use this model then you have to preprocessing the data to use this model.(use LayoutLMv2Processor models)
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## Process
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1. I used Korean language invoice document image data to training this model
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2. Use Naver Clova service for extract text data from images
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3. Determining text Label(target) for each text box
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4. Combining the image text, bounding box position data, Label
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5. And use LayoutLMv2Processor models for encoding the data
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6. Do prediction for encoded data to this model
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