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Marker: Datalab's Open-Source Pipeline Parser — Faster, CPU-Ready, More Accurate

Marker (Datalab open source, Apache 2.0 license, v2.0.0 released 2026-07-20, 39.5k stars) is a pipeline-style document parsing standard library that outputs Markdown and JSON, supports optional LLM boost (`--use_llm`, default `gemini-3.5-flash`), custom formatting logic, table/formula/inline-math/link/reference/code formatting, image extraction and preservation, header/footer removal, and runs on pure CPU, GPU, or MPS (`Apple Silicon`). Unlike [Docling](/posts/tech/2026-09-06-docling-document-parsing) (structured JSON core, dedicated XML exports, pure MIT), Marker centers on Markdown/JSON under `Apache 2.0` with a separate model-weight license (`AI Pubs Open Rail-M`, $5M commercial threshold); unlike [MinerU](/posts/tech/2026-09-05-mineru-ocr-doc-parsing) (custom agreement with MAU/revenue thresholds + attribution obligations), Marker offers a simpler licensing story (`Apache 2.0` code) but requires accepting a separate model-weight license for weights. The series framework ([three-layer model](/posts/ai/2026-08-06-document-parsing-three-layers)) positions all three (`MinerU`, `Docling`, `Marker`) as pipeline-based parsing-layer options with distinct licensing, output, and speed trade-offs.

Docling: IBM's Open-Source, MIT-Licensed Document Parsing Standard Library Built on Structured JSON

Docling (IBM Research Zurich, now governed by the Linux Foundation AI & Data, MIT license, v2.100.0 released 2026-06-09) is an open-source document parsing standard library with structured JSON (DoclingDocument) as its core output. It supports PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages, video (MP4/AVI/MOV with ASR transcription and keyframes), audio (WAV/MP3), email (EML/MSG), ODF, and XBRL financial reports, with a swappable-stage pipeline parser (pure CPU or GPU-accelerated), VlmPipeline option (GraniteDocling 258M VLM), MCP server and API server (docling-serve), and native integrations with LangChain, LlamaIndex, Crew AI, and Haystack.

MinerU:從 PDF 到 Markdown 的開源文件解析引擎,為 RAG 與 LLM 訓練而生

MinerU(OpenDataLab / opendatalab/MinerU)是一個開源文件解析引擎,把 PDF、圖像、DOCX、PPTX、XLSX 轉成結構化 Markdown 與 JSON,內建公式識別、表格提取與 109 語言 OCR,起源於 InternLM 前訓練階段,適合 RAG 前處理與知識庫建構。

The Deterministic Extraction Layer: Solve 80% of Your PDFs With No Model At All

Digital-native PDFs already contain readable text — what's missing is structure, and heuristics can recover it. PyMuPDF, pdfplumber, pypdf, and Tika do this with zero GPU and zero inference cost. The biggest selection trap isn't accuracy; it's PyMuPDF's AGPL-3.0 license.