Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
TL;DR
Digitization projects in the humanities create large collections of historical documents that are difficult to sort manually. A page image classifier was developed to automatically categorize scanned images into text, tables, and graphics.
✦ Why It Matters
Engineers can leverage this classifier to streamline the processing of historical documents in digitization projects.
Key Takeaways
- Utilize the fine-tuned RegNetY-16GF model for high-accuracy classification.
- Access the open-source models and dataset for your own projects.
- Consider automated classification to handle large archival datasets efficiently.
- Explore content-specific processing options like OCR for improved data extraction.
How It Works
The classifier employs deep learning architectures, particularly CNNs and Vision Transformers, to analyze visual content in scanned documents. By fine-tuning these models on a large, annotated dataset, the system learns to distinguish between different content types, such as text, tables, and graphics, achieving high accuracy.