text-extract-api vs Xberg

Side-by-side comparison of two AI agent tools

text-extract-apiopen-source

Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSO

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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

text-extract-apiXberg
Stars3.2k9.4k
Star velocity /mo16.844919786096256779.9166666666666
Commits (90d)03.1k
Releases (6m)010
Overall score0.21380900278600150.8100951718666431

Pros

  • +完全本地化处理,无外部依赖,确保数据隐私和安全性
  • +支持多种先进OCR策略(LLaMA Vision、EasyOCR等),识别精度极高
  • +集成分布式队列和缓存机制,支持大规模文档批量处理

    Cons

    • -需要安装多个依赖组件(Docker、Ollama),初始设置较为复杂
    • -本地运行PyTorch模型需要较大计算资源和存储空间

      Use Cases

      • •医疗机构将MRI报告、病历等医疗文档转换为结构化数据
      • •企业财务部门处理发票、合同等文档并自动移除敏感信息
      • •法律机构批量数字化和分析大量合规文档或法律条文

        FAQ

        Which is more popular, text-extract-api or Xberg?
        Xberg has more GitHub stars (9,359 vs 3,182).
        Which is more actively developed, text-extract-api or Xberg?
        Xberg had more commits in the last 90 days (3,122 vs 0).
        Should I use text-extract-api 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.