PDF to Structured Markdown
Layout-aware PDF conversion that preserves reading order, tables, and headings — the clean RAG ingestion layer for your knowledge pipeline.
What does this workflow do?
System flow
PDF 页面渲染 → 版面分析（Docling 版面检测模型）→ 段落/表格/图表/公式区域识别 → OCR 增强（扫描件自动降噪）→ Markdown 语法还原（标题层级/列表/表格）→ 元数据提取（标题/作者/目录/参考文献）
Pipeline breakdown
- Pillow
pillow— Image pre-processing — deskew, denoise, crop and normalise before analysis pdf → image - Docling
docling— Layout-aware document parsing that preserves reading order and tables pdf → markdown - PyPDF
pypdf— PDF text and metadata extraction, page-level splitting pdf → json
Workflow Reputation Score
Based on executions, success rate, update frequency, component quality, and user rating.
Based on executions, success rate, update frequency, component quality, and user rating.
Components used
Image pre-processing — deskew, denoise, crop and normalise before analysis
python-pillow/Pillow ↗Layout-aware document parsing that preserves reading order and tables
DS4SD/docling ↗How is this different from n8n or Zapier?
Unlike generic workflow builders, FlowSync workflows ship pre-configured with AI skills baked into every node — OCR, LLM reasoning, transcription, super-resolution — not just webhook triggers. Each node is white-box auditable: you see input, output, and config. Deployment is instant — no self-hosting, no API-key per node.
Use cases
- 论文批量入库 RAG 系统
- 法律合同全文检索
- 技术文档知识库建设
- 政府报告结构化归档
Author & Source
Field notes
每天读 5 篇论文，Scholarcy 一年 $120。这个免费版效果一样，Markdown 质量极高。
几百页行业报告 1 分钟转 Markdown，进知识库做 RAG 检索。效果惊艳。
Data handling
FlowSync Workflow Marketplace · 67 reputation score · 25% execution · 93% success · 3-node white-box pipeline · maintained by FlowSync Official
Ready to run?
Layout-aware PDF conversion that preserves reading order, tables, and headings — the clean RAG ingestion layer for your knowledge pipeline.