olmocr vs Xberg

Side-by-side comparison of two AI agent tools

olmocropen-source

Toolkit for linearizing PDFs for LLM datasets/training

X
Xbergopen-source

Polyglot document intelligence with a Rust core: extract text, metadata, images, tables, and structured data from 106 formats across 140 file extensions, plus c

Metrics

olmocrXberg
Stars19.7k9.4k
Star velocity /mo419.3582887700535779.9166666666666
Commits (90d)03.1k
Releases (6m)010
Overall score0.294547411519518350.8100951718666431

Pros

  • +Excellent handling of complex document layouts including equations, tables, handwriting, and multi-column formats with natural reading order preservation
  • +Cost-effective processing at under $200 per million pages, making it economical for large-scale dataset creation
  • +Continuous model improvements with recent releases showing significant performance gains and reduced hallucinations on blank documents

    Cons

    • -Requires GPU resources due to 7B parameter model, making it computationally intensive and potentially expensive to run
    • -May require multiple retries for some documents to achieve optimal results
    • -Limited to image-based document formats (PDF, PNG, JPEG) and requires technical expertise for setup and optimization

      Use Cases

      • •Converting academic papers and research documents with complex equations and figures for LLM training datasets
      • •Processing legacy document archives with multi-column layouts and mixed content types into searchable text format
      • •Creating high-quality training data from technical manuals, textbooks, and scientific publications for domain-specific language models

        FAQ

        Which is more popular, olmocr or Xberg?
        olmocr has more GitHub stars (19,683 vs 9,359).
        Which is more actively developed, olmocr or Xberg?
        Xberg had more commits in the last 90 days (3,122 vs 0).
        Should I use olmocr or Xberg?
        Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.