SmolDocling - The SmolOCR Solution?
AI Summary
Overview
- Introduction of Small Dockling: A new OCR model from Hugging Face in partnership with IBM.
Model Characteristics
- Size: 256 million parameters, designed for low VRAM GPUs.
- Performance: Claims to outperform competitors by up to 27x, though the models tested excluded several known industry benchmarks.
Functionalities
- Document Understanding: Not just OCR but includes document extraction and conversion.
- Supported Formats: PDFs, Word files, HTML, images, etc.
- Outputs: Provides structured outputs (dock tags format) indicating types of content (text, images, tables, etc.) and their positions in documents.
- Architecture: Based on a standard VLM architecture with a significant parameter distribution (93M + 135M + projection layers).
Practical Application
- Demos available for testing.
- Can be run using the Transformers or VLM library for faster inference.
- Potential for fine-tuning specific tasks for better performance in niche applications.
Performance Insights
- While effective for document-specific tasks, it may not match larger OCR models like M OCR or Mistral for general use.
- Encouraged users to create their own labeled datasets for fine-tuning the model to specific needs.
Conclusion
- The Small Dockling model presents a promising option for developing customized document conversion pipelines, especially for users willing to invest time in personalization.