Zerox vs Reducto — Comparison

Zerox

OCR & Document Extraction using vision models. Contribute to getomni-ai/zerox development by creating an account on GitHub.

A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense! Zerox is available as both a Node and Python package. (Node.js SDK - supports vision models from different providers like OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Google Gemini, etc.) The maintainFormat option tries to return the markdown in a consistent format by passing the output of a prior page in as additional context for the next page. This requires the requests to run synchronously, so it's a lot slower. But valuable if your documents have a lot of tabular data, or frequently have tables that cross pages. Zerox supports structured data extraction from documents using a schema. This allows you to pull specific information from documents in a structured format instead of getting the full markdown conversion. Use extractPerPage to extract data per page instead of from the whole document at once. Zerox supports a wide range of models across different providers: (Python SDK - supports vision models from different providers like OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, etc.) The pyzerox.zerox function is an asynchronous API that performs OCR (Optical Character Recognition) to markdown using vision models. It processes PDF files and converts them into markdown format. Make sure to set up the environment variables for the model and the model provider before using this API. Refer to the LiteLLM Documentation for setting up the environment and passing the correct model name. Note the output is manually wrapped for this documentation for better readability. This project is licensed under the MIT License. OCR Document Extraction using vision models There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

Reducto

pages processed

Reducto's parser reads documents like a human would—capturing layout, structure, and meaning with high accuracy. Our Agentic OCR reviews and corrects outputs in real-time for near-perfect results, even on edge cases. Reducto's parser reads documents like a human would—capturing layout, structure, and meaning with high accuracy. Our Agentic OCR reviews and corrects outputs in real-time for near-perfect results, even on edge cases. Automatically separate multi-document files or long forms into individually useful units. Intelligent heuristics and layout-aware splitting keep your pipelines clean and efficient—no manual pre-processing needed. Extract structured data directly from documents with schema-level precision. Whether it's invoice fields, onboarding forms, or financial disclosures, Reducto ensures the right data lands exactly where you need it. Fill in detected blanks, tables, and checkboxes with supplied data. No bounding boxes or pre-defined templates are required; Edit dynamically identifies fillable elements regardless of document layout or format, supporting scanned PDFs, digital forms, and complex multi-page documents. Reducto helped us parse documents we previously could not because of table complexity. It's probably the only AI product that has actually worked for us. Reducto first uses layout-aware models to break down the document visually, capturing regions, tables, figures, and text. VLMs make corrections to mistakes Like a human editor, our Agentic model can detect minor mistakes and correct them, ensuring accuracy even in the most detailed cases. VLMs review Reducto's outputs Vision-language models then interpret each region in context—linking labels to values, understanding tables, and classifying segments. Everything else you need to make your data LLM-ready. Battle-tested infrastructure you can trust in production and at scale. Hands-on forward deployed support and tailored SLAs to meet your enterprise needs. Run Reducto entirely within your own infrastructure—ideal for strict security, compliance, and data residency requirements. Widely trusted by enterprises worldwide

Zerox

Reducto

Zerox

Reducto

Zerox

Reducto