prescription
To automate the processing of documents based on InternVL, you can follow the steps below:
- environmental preparation: First make sure that your system meets the GPU memory requirements (16GB for 8B models), and follow the guidelines in the text to install the Python 3.9 environment and all dependencies, including the document-specific processing module (pip install -r requirements/classification.txt).
- Model Selection: Select the appropriate model size based on document complexity:
- Simple documents (e.g., invoices) use 1B parametric models
- 8B or larger model recommended for complex documents (legal contracts)
- Core Operating Procedures::
- Loading scanned document images using the load_image function
- Construct specific prompts (e.g., "Extract the amount and date from this invoice")
- Send a request for structured data through the pipeline interface
- Advanced Techniques::
- Multi-Page Document Processing: Split PDF into single-page images for batch processing
- Table recognition: Use explicit instructions such as "Please output table content in JSON format".
- Quality validation: filtering low-quality identifications by confidence thresholds
Typical application scenarios include contract key terms extraction, financial statement digitization, medical record structuring, etc. Compared with traditional OCR solutions, the accuracy rate can be improved by more than 30%. For enterprise-level applications, it is recommended to realize system integration by deploying LMDeploy as an API service.
This answer comes from the articleInternVL: Open Source Multimodal Large Model with Image, Video and Text Processing SupportThe
May not be reproduced without permission:AI productivity tools " How to realize efficient document automation based on InternVL?
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