Marker is a comprehensive document processing platform designed to automate the conversion, extraction, and structuring of data from complex files. It functions as an orchestration engine that chains modular processing steps into versioned, reusable pipelines, allowing organizations to standardize document handling and automate repetitive business tasks at scale. The platform distinguishes itself through its support for secure, private infrastructure deployment, enabling users to run containerized services within their own environments to maintain strict data privacy. It features specialized
Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t
sChandra is a document processing platform that converts images, PDFs, Word documents, spreadsheets, and other formats into structured output such as HTML, Markdown, or JSON while preserving layout. It can also extract specific data fields from invoices, contracts, or reports using user-defined JSON schemas, with citations back to source locations. The service supports form filling in PDF and image documents, document generation from Markdown, and extraction of tracked changes from Word files. The platform distinguishes itself with pipeline-based processing chains that combine multiple proces
Unstract is an unstructured data extraction system and ETL pipeline orchestrator that uses large language models to convert documents, images, and scans into structured JSON. It provides a document extraction API for integrating these capabilities into external automation tools and includes a Model Context Protocol server to connect AI agents to structured information retrieval. The system ensures data accuracy through a verification tool featuring dual-model verification and human-in-the-loop review with coordinate-based document highlighting. It utilizes natural language extraction schemas
Surya is a document processing platform designed to transform unstructured files into structured, machine-readable data. It provides a comprehensive suite of tools for text recognition, layout analysis, and reading order detection, enabling the conversion of PDFs and images into formats such as JSON, HTML, or markdown. The platform is built to handle complex document workflows, offering capabilities for data extraction, document segmentation, and automated form completion.
Les fonctionnalités principales de datalab-to/surya sont : Document Analysis, Document Conversion, Document and Unstructured Extraction, Structured Data Extraction, Document Automation Pipelines, Data Pipeline Orchestration, Document Processing Pipelines, Pipeline Orchestration.
Les alternatives open-source à datalab-to/surya incluent : datalab-to/marker — Marker is a comprehensive document processing platform designed to automate the conversion, extraction, and… unstructured-io/unstructured — Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into… datalab-to/chandra — sChandra is a document processing platform that converts images, PDFs, Word documents, spreadsheets, and other formats… zipstack/unstract — Unstract is an unstructured data extraction system and ETL pipeline orchestrator that uses large language models to… elysiajs/elysia — Elysia is a high-performance TypeScript web framework designed for building type-safe backend services. It provides a… google-gemini/cookbook — The Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides…