DocStrange

DocStrange is a powerful, open-source Python library designed to transform complex, unstructured documents—including PDFs, images, spreadsheets, and presentations—into clean, usable data formats optimized for Artificial Intelligence (AI) applications. It solves the critical problem of preparing diverse content for downstream AI workflows, such as Retrieval-Augmented Generation (RAG) pipelines, by delivering highly accurate, structured output. If you are a developer or data scientist building robust LLM applications, DocStrange provides the essential foundation for high-quality input data.

Key Features

DocStrange provides an end-to-end processing pipeline, ensuring that the output preserves critical document structure while eliminating noise and artifacts.

📄 Universal Input & Flexible Output

DocStrange accepts a comprehensive range of file types, including PDF, image (JPEG, PNG), PPTX, DOCX, XLSX, and web URLs, streamlining your ingestion process. It delivers output in formats specifically engineered for AI consumption: LLM-optimized Markdown, structured JSON (with schema support), HTML, and CSV. This flexibility ensures your source material is immediately ready for vector databases or prompt engineering.

🧠 Intelligent Structured Extraction

Move beyond simple text scraping. DocStrange allows you to define specific fields or enforce a nested JSON schema, ensuring the output data is consistently structured. This capability is powered by an upgraded 7B model for higher accuracy and deeper document understanding, enabling precise extraction of entities, relationships, and key metrics from complex forms or contracts.

🔎 Advanced OCR and Artifact Removal

Working with scanned documents, phone photos, or receipts often introduces noise that degrades AI performance. DocStrange incorporates an advanced OCR pipeline with multiple engine fallbacks to accurately extract text from even poor-quality images. It automatically cleans the output by removing page artifacts and headers, ensuring the final text is clean, coherent, and highly readable for language models.

📊 Accurate Table and Structure Recognition

Tables are notoriously difficult for standard parsers. DocStrange excels at accurately identifying and formatting tables, converting them into clean, LLM-optimized Markdown tables. This preservation of structural context is crucial, allowing LLMs to correctly interpret relationships between data points rather than treating tables as flat, jumbled text blocks.

Use Cases

DocStrange is built for scenarios demanding high data quality, structural integrity, and processing privacy.

1. Building Robust RAG Pipelines

Quickly convert entire libraries of complex documents (e.g., regulatory PDFs, internal knowledge bases, technical manuals) into clean, chunkable LLM-Ready Markdown. By providing clean, structured input, you significantly reduce the noise in your retrieval process, leading to higher quality answers and reduced hallucinations in your RAG system.

2. Automated Financial and Legal Data Processing

Use the structured JSON extraction capability to automate the intake of forms, invoices, and contracts. For instance, you can define a schema to extract invoice_number, vendor_name, and total_amount from a batch of scanned invoices, transforming unstructured images into clean, database-ready data without manual intervention.

3. Ensuring Data Privacy and Compliance

For organizations handling sensitive or proprietary documents, DocStrange offers a 100% private, local mode. You can run the entire conversion pipeline—including the 7B model, OCR, and layout analysis—on your own CPU or GPU infrastructure, ensuring zero data transmission to external cloud services and maintaining full compliance control.

Unique Advantages

DocStrange differentiates itself not just through its features, but through its architectural approach, offering a level of control and quality unique among document processing tools.

• Complete Local Processing Control: Unlike general-purpose cloud AI services (e.g., AWS Textract), DocStrange provides a fully functional, local processing option. This gives you complete control over your data pipeline, latency, and operational costs while guaranteeing data privacy.

• Ready-to-Use End-to-End Pipeline: DocStrange is a robust, integrated parsing solution, not just a flexible framework like LangChain. It handles the complex orchestration of OCR, layout detection, table extraction, and final output formatting internally, saving you the significant development time required to build and tune these components yourself.

• Superior Handling of Scans and Photos: Many document parsers struggle with non-native digital PDFs. DocStrange is specifically built to deliver high-quality results from difficult inputs like low-resolution scans and phone photos, minimizing errors where high-fidelity OCR is essential.

Conclusion

DocStrange delivers the accuracy, structure, and control necessary to transform the most challenging document formats into AI-ready data. By providing clean, LLM-optimized output, you accelerate your development cycle and ensure the highest quality results for your RAG pipelines and intelligent applications.