To ensure the best results, the following points need to be noted:
- Input quality: The resolution of the image is recommended to be above 300dpi, avoiding strong light reflection. Handwriting should be clear
- Hardware configuration: At least 4GB of memory is required to process A4-sized documents, and cropping is recommended for very large files.
- parameterization: Complex documents need to increase the value of max_new_tokens, which can be set to 16384 for dense forms.
Common Problem Solving:
- Missing content: check if the token limit is reached, or if the image is distorted
- Formatting errors: update the docling_core library to the latest version.
- GPU not enabled: Make sure PyTorch for CUDA is installed!
For enterprise level applications, it is recommended:
- Establishment of image pre-processing flow (automatic cropping/enhancement)
- Fine-tuning of prompt templates for specific document types
- Periodically clear the model cache (stored by default in ~/.cache/huggingface/)
This answer comes from the articleSmolDocling: a visual language model for efficient document processing in a small volumeThe
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