markerDatalab's 39k-star PDF-to-Markdown/JSON converter: a layout pipeline plus an optional LLM pass for tables, forms and equations, with chunk output and form-value extraction built in.
Why switchSame shop, two tiers: marker is the pipeline you run at volume with selective LLM assist, chandra is the vision-language model you reach for when accuracy on scans, handwriting and dense tables matters more than throughput.
Full comparison → olmocrOpen toolkit that linearizes messy PDFs — scans, tables, equations, handwriting — into clean ordered Markdown with a self-hosted vision-language model. Built for LLM training data and RAG ingestion.
Why switchHead-to-head open OCR-VLM rivals on the same benchmark (Chandra 2 scores 85.8 vs olmOCR 2's 82.4). olmOCR is fully permissive and tuned for LLM-training-data linearization; Chandra leads on handwriting/forms/multilingual but carries an OpenRAIL-M weights license.
Full comparison → doctrdocTR: two-stage OCR in PyTorch — detect words, then recognize them — with pretrained detection and recognition architectures you can mix, plus layout detection and rotated-page handling.
Why switchClassic pipeline versus one big model: Chandra is a VLM that reads a page into structured HTML/Markdown including handwriting and math; docTR runs small detection and recognition nets for word boxes. Chandra for structure, docTR for cost, determinism and fine-tuning.
Full comparison → Unlimited-OCRBaidu's open OCR VLM that parses entire multi-page documents in one shot — 'unlimited' long-horizon parsing pushing DeepSeek-OCR further. MIT weights on HF; serve via transformers, vLLM or SGLang.
Why switchBoth are open OCR vision-language models. Baidu's model bets on one-shot long-horizon parsing of entire multi-page documents; Chandra processes per-page with stronger layout/table/form structure and a broader language benchmark.
Full comparison → MinerUHeavyweight document-to-markdown/JSON parser — PDFs plus Office (docx/pptx/xlsx) through layout analysis and OCR into LLM-ready output for RAG and agentic pipelines. 73k stars, self-hostable.
Why switchOverlapping document-to-markdown job at different layers: MinerU is a full parsing pipeline (layout analysis + OCR + export) you deploy as tooling; Chandra is the single end-to-end OCR model you'd slot into such a pipeline.
Full comparison → chunkrDocument intelligence API in Rust: layout analysis, OCR with bounding boxes, and semantic chunking that turn PDFs, PPTs and Word docs into RAG-ready structured chunks.
Why switchOverlapping document-intelligence job at different layers: chunkr is the deployed pipeline API (layout + OCR + chunking); Chandra is the end-to-end OCR model you would slot into such a pipeline.
Full comparison →