Zerox vs Google Document AI — 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.

Google Document AI

The Document AI solutions suite includes pretrained models for document processing, Workbench for custom models, and Warehouse to search and store.

Create document processors that help automate tedious tasks, improve data extraction, and gain deeper insights from unstructured or structured document information. Document AI helps developers create high-accuracy processors to extract, classify, and split documents. Seamlessly connect to BigQuery, Vertex Search, and other Google Cloud products Enterprise-ready, along with Google Cloud's data security and privacy commitments Built for developers; use the UI or API to easily create document processors Use generative AI to extract data or classify documents out of the box, with no training necessary to get started. Simply post a document to an enterprise-ready API endpoint to get structured data in return. Document AI is powered by the latest foundation models, tuned for document tasks. Also, with powerful fine-tuning and auto-labeling features, the platform offers multiple paths to reach the required accuracy. Structure and digitize information from documents to drive deeper insights using generative AI to help businesses make better decisions. Extract data from your documents using generative AI.  For full product capabilities head to Document AI in the Google Cloud Console. Document AI Workbench provides an easy way to build custom processors to classify, split, and extract structured data from documents. Workbench is powered by generative AI, which means it can be used out of the box to get accurate results across a wide array of documents. Furthermore, you can achieve higher accuracy by providing as few as 10 documents to fine-tune the large model—all with a simple click of a button or an API call. With Enterprise Document OCR, users gain access to 25 years of optical character recognition (OCR) research at Google. OCR is powered by models trained on business documents and can detect text in PDFs and images of scanned documents in 200+ languages. The product can see the structure of a document to identify layout characteristics like blocks, paragraphs, lines, words, and symbols. Advanced features include best-in-class handwriting recognition (50 languages), recognizing math formulas, detecting font-style information, and extracting selection marks like checkboxes and radio buttons. Try Document OCR now for accurate text and layout extraction. Developers use Form Parser to capture fields and values from standard forms, to extract generic entities, including names, addresses, and prices, and to structure data contained in tables. This product works out of the box and does not require any training or customization and is useful across a broad range of document customization. Explore document processing with Form Parser. Try out pretrained models for commonly used document types including W2, paystub, bank statement, invoice, expense, US driver license, US passport, and identity proofing. Explore pretrained options in the processor gallery. Document AI is helping customers improve fraud detection, automate customer support, and pro

Zerox

Google Document AI

Zerox

Google Document AI

Zerox

Google Document AI