document parsing / OCR / structured data extraction for LLMs
Unstructured is an open-source ETL tool that converts complex documents (PDF, DOCX, images, etc.) into clean structured formats for LLMs via OCR, layout analysis, and partitioning.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
when you need a library that directly OCRs and converts PDFs/images/Office files to Markdown/JSON/CSV/HTML with MIT license.
Extract and convert data from any document, images, pdfs, word doc, ppt or URL into multiple formats (Markdown, JSON, CSV, HTML) with intelligent structured data extraction and advanced OCR.
when you need a mature, MIT-licensed document conversion library with strong PDF/Office support and active maintenance.
Get your documents ready for gen AI
when you want a high-quality PDF/Office-to-Markdown/JSON converter with OCR and table/LaTeX support for LLM workflows.
Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
when you want ORM-style, LLM-driven schema-based extraction rather than generic document partitioning.
ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.
when you need Rust-core performance and bindings for multiple languages instead of a Python-native parser.
Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
when you want a self-hosted Node.js service that converts documents to enriched structured data instead of a Python library.
Transforms PDF, Documents and Images into Enriched Structured Data
when you need a fast Rust-based document parser with a minimal API from the LlamaIndex ecosystem.
A fast, helpful, and open-source document parser
when you want an OCR-centric toolkit with huge language coverage and layout/table recognition rather than a full ETL pipeline.
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
when you want vision-model-based OCR and extraction for complex documents instead of traditional segmentation/parsing.
OCR & Document Extraction using vision models
when you need a customizable document AI framework for building your own extraction pipelines with DL models.
A Repo For Document AI
when you need an orchestration framework that integrates LLM/VLLM pipelines for complex document extraction.
Complex data extraction and orchestration framework designed for processing unstructured documents. It integrates AI-powered document pipelines (GenAI, LLM, VLLM) into your applications, supporting various tasks such as document cleanup, optical character recognition (OCR), classification, splitting, named entity recognition, and form processing
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
when you need specialized OCR for historical/handwritten documents rather than general-purpose parsing.
CHURRO is an OCR toolkit for historical document transcription, built to make handwritten and printed sources readable at high accuracy and lower cost.
when you need a pipeline focused on academic/technical papers with arXiv and GROBID integration.
A Python pipeline tool and plugin ecosystem for processing technical documents. Process papers from arXiv, SemanticScholar, PDF, with GROBID, LangChain, listen as podcast. Customize your own pipelines.
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
when you want a ready-made web/UI tool for document parsing rather than embedding a library.
Part of the KDAN ecosystem, DocSlight offers document parsing, OCR, and data extraction that turn PDFs, scans, images, and Office files into structured outputs for RAG pipelines, AI agents, and enterprise document automation.
when you want a hosted/GUI document extraction tool from the ComPDF ecosystem instead of a code library.
With DocSlight, precisely parse and extract data from any document, including PDFs, scans, images, and Office files. It is an open-source AI project from ComPDF (KDAN ecosystem).
when you want a zero-dependency CLI or agent skill that wraps a parser for easy use, not a library to integrate.
AI-Native document parser: PDF, Office & images → clean Markdown with LaTeX, tables & OCR. Zero-dependency CLI & skill for Claude Code, Cursor & AI agents.
These projects were analysed and named unstructured among their alternatives. The relationship is not symmetric — how unstructured rates them is a separate judgement, made when unstructured is analysed in its own right.
calls unstructured “when you want a full ETL platform with connectors, API, and pre-built chunking.”
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
calls unstructured “Direct Python alternative, use when you want a more established ETL-oriented parser.”
Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
calls unstructured “if you need an ETL-oriented document processing pipeline with data connectors”
Get your documents ready for gen AI
calls unstructured “When you need an ETL pipeline with connectors to many data sources and output formats.”
Convert PDF to markdown + JSON quickly with high accuracy
calls unstructured “Choose if you need an ETL pipeline with connectors for many document types; it is heavier and may require separate processing for accessibility.”
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.
calls unstructured “Python ETL for document structuring; choose when you need data pipeline integrations.”
Convert Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF to clean Markdown. Built in Rust, with Node.js and Python bindings.
calls unstructured “Python ETL library with active development, outputs clean structured data for LLM pipelines.”
Transforms PDF, Documents and Images into Enriched Structured Data
calls unstructured “choose when you need a production-grade ETL with API connectors, partitioning, and LangChain/vector-store integration; seed is simpler but less ecosystem-connected.”
Extract and convert data from any document, images, pdfs, word doc, ppt or URL into multiple formats (Markdown, JSON, CSV, HTML) with intelligent structured data extraction and advanced OCR.
calls unstructured “Document ETL pipeline; choose if you want connectors and chunking for LLM ingestion.”
Unlimited OCR Works: Welcome the Era of One-shot Long-horizon Parsing.
calls unstructured “when you need a full ETL pipeline for documents with pluggable backends (not vision-only).”
OCR & Document Extraction using vision models
calls unstructured “choose when you want a production ETL system for documents with file-type connectors and vector-database output.”
Multilingual Document Layout Parsing in a Single Vision-Language Model
calls unstructured “choose when you want an ETL pipeline to turn documents into structured data for LLMs, using a broader toolset rather than an OCR-free model.”
Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
calls unstructured “ETL for transforming documents into structured formats for LLMs”
A lightweight LMM-based Document Parsing Model
calls unstructured “ETL for document structuring with multiple OCR backends; choose when you need to handle many document types beyond just PDFs.”
PDF craft can convert PDF files into various other formats. This project will focus on processing PDF files of scanned books.
calls unstructured “Use for ETL-style document processing into structured data for LLMs.”
Enhances Tesseract OCR output using LLMs (local or API) for error correction, smart chunking, and markdown formatting of scanned PDFs