Why LLMs still have problems with OCR
📰 Hacker News · ritvikpandey21
LLMs struggle with OCR due to nondeterministic outputs and confidence issues in multistep ingestion pipelines
Action Steps
- Build a document ingestion pipeline using tools like Tesseract or Pytesseract to test OCR capabilities
- Run experiments to measure the confidence of LLM outputs on various document types and sizes
- Configure the pipeline to handle nondeterministic outputs and maintain confidence across millions of pages
- Test the pipeline with different LLM models and fine-tune them for improved performance
- Compare the results of different pipeline configurations and LLM models to identify areas for improvement
Who Needs to Know This
NLP engineers and researchers working with LLMs and document ingestion pipelines can benefit from understanding these challenges to improve their models and workflows
Key Insight
💡 Maintaining confidence in LLM outputs is crucial for reliable document ingestion pipelines
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🤖 LLMs still struggle with OCR due to nondeterministic outputs and confidence issues 📊
Full Article
Document ingestion and the launch of Gemini 2.0 caused a lot of buzz this week. As a team building in this space, this is something we researched thoroughly. Here’s our take: ingestion is a multistep pipeline, and maintaining confidence from LLM nondeterministic outputs over millions of pages is a problem.
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