Zerox offers robust integration with various OCR vision models, focusing on structured document extraction and markdown conversion, ideal for AI data ingestion with complex layouts. In contrast, Textract provides scalable, secure data extraction from a wide range of documents, leveraging AWS infrastructure for high accuracy and business efficiency. Textract offers a competitive pricing model with free-tier options, making it suitable for large-scale operations.
Best for
Zerox is the better choice when your team requires specific structured data extraction from documents with complex layouts and values seamless integration with AI vision models.
Best for
Textract is the better choice when your team works with large volumes of documents requiring high accuracy, compliance, and integration with AWS services for scalable data processing solutions.
Key Differences
Verdict
Choose Zerox if your organization needs specialized extraction capabilities for documents with non-standard formats and prefers a tool that prioritizes AI-driven integration. Opt for Textract if your priority is handling high-volume document processing with robust infrastructure support, especially if already using AWS services. Textract's cost-effective pricing model and free tier make it appealing for large-scale operations.
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.
Textract
Amazon Textract is a machine learning (ML) service that uses optical character recognition (OCR) to automatically extract text, handwriting, and data
Automatically extract printed text, handwriting, layout elements, and data from any document Drive higher business efficiency and faster decision-making while reducing costs. Extract key insights with high accuracy from virtually any document. Scale up or scale down the document processing pipeline to quickly adapt to market demands. Securely automate data processing with data privacy, encryption, and compliance standards. Accurately extract critical business data such as mortgage rates, applicant names, and invoice totals across a variety of financial forms to process loan and mortgage applications in minutes. Better serve your patients and insurers by extracting important patient data from health intake forms, insurance claims, and pre-authorization forms. Keep data organized and in its original context, and remove manual review of output. Easily extract relevant data from government-related forms, such as small business loans, federal tax forms, and business applications, with a high degree of accuracy. As part of the AWS Free Tier, you can get started with Amazon Textract for free. The Free Tier lasts for three months, and new AWS customers can analyze up to: Total pages processed = 100,000 Total pages processed = 2,000,000 Price per page = $0.0015 for first 1 million and $0.0006 for pages after 1 million Total pages processed = 5,000 pages Price for page with table = $0.015 Price for page with form (key-value pair) = $0.05 Price per page with Queries = $0.015 Total pages processed = 2,000,000 pages Price for page with Tables, Forms and Queries = $0.070 for the first one million and $0.055 for the next one million Let’s assume you want to extract data from 100,000 invoices using the Analyze Expense API. The pricing per page in the US West (Oregon) region for 1 million pages is $0.01 and you process 100,000 invoices. The total cost would be $1,000. See the calculation below: Total pages processed = 100,000 Let’s assume you want to extract data from 1,500,000 invoices using the Analyze Expense API. The pricing per page in the US West (Oregon) region for one million pages is $0.01 per page and $0.008 per page after one million. The total cost would be $14,000. See the calculation below: Total pages processed = 1,500,000 Price per page = $0.01 for the first 1 million and $0.008 for the next 500,000 Let’s say you want to extract information from 100,000 identity documents using the Analyze ID API. The pricing per page in the US West (Oregon) Region for 100,000 pages is $0.025 per page for up to 100,000 pages. The total cost would be $2,500. Total pages processed = 100,000 Let’s say you want to extract information from 600,000 identity documents using the Analyze ID API. The pricing per page in the US West (Oregon) Region for 100,000 pages is $0.025 per page and $0.01 per page after 100,000. The total cost would be $7,500. Total pages processed = 600,000 Let’s say you want to extract information from 200,000 pages of mort
Zerox
-25% vs last weekTextract
Not enough dataZerox
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Pricing found: $50.10, $48.71, $48.71, $48.71, $9.74
Textract
Pricing found: $0.0015,, $150., $0.0015, $0.0015, $150
Only in Zerox (10)
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Zerox
Web & game developers, this is your jam. Gamedev.js Jam 2026 is back. 🎮 🗓 April 13-26, 2026 🌐 Build an HTML5 game in 13 days 🏆 Prizes + expert feedback 💬 Active community + Discord Theme reve
Web & game developers, this is your jam. Gamedev.js Jam 2026 is back. 🎮 🗓 April 13-26, 2026 🌐 Build an HTML5 game in 13 days 🏆 Prizes + expert feedback 💬 Active community + Discord Theme revealed on day one. Ship something weird. Ship something fun. Just ship it. https://t.co/KPphbM8rTz
Textract
Zerox
Textract
Zerox is better suited for complex document layouts as it offers structured data extraction and integration with various vision models.
While Zerox uses tiered pricing starting at $9.74, Textract's pricing is subscription-based, offering a competitive per-page cost with a free tier option for up to 100,000 pages.
Textract likely benefits from a broader support community due to its integration with AWS, whereas Zerox may have more focused, but smaller community discussions on platforms like GitHub.
While possible to use both tools in a pipeline, it's typically more efficient to choose one based on specific requirements; Zerox for complex layout extraction and Textract for scalable volume processing.
Textract may be easier to start with for AWS users thanks to its seamless integration and free-tier offering, while Zerox requires setup with environment variables for model selection.
