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Document Extraction Model

Google Document AI

Google Document AI is a cloud-based document extraction service from Google that uses OCR to parse and structure document content.

PublisherGoogle
TypeDocument Extraction
Released2024
Price$1.50/1K pages

OCR and document extraction from Google Cloud

Google Document AI is a managed cloud service offered by Google Cloud that applies optical character recognition and machine learning to extract structured data from documents. It can process a wide range of document types, including forms, invoices, contracts, and identity documents, returning structured fields and values rather than raw text. The service is part of the Google Cloud platform and is accessed via API, making it suitable for integration into automated document processing pipelines.

Google Document AI is designed for organizations that need to digitize and extract information from large volumes of documents at scale. It includes specialized processors for specific document types, such as expense reports, tax forms, and lending documents, in addition to a general-purpose OCR processor. The service is well-suited for use cases like accounts payable automation, mortgage processing, and any workflow that requires converting unstructured document content into structured, queryable data.

What Google Document AI supports

Optical Character Recognition

Extracts printed and handwritten text from scanned documents and images, returning character-level and word-level bounding box coordinates alongside the recognized text.

Structured Data Extraction

Identifies and returns named fields and their values from documents such as invoices, forms, and receipts, outputting results in structured JSON format.

Specialized Document Processors

Provides pre-built processors trained on specific document types including W-2 tax forms, pay stubs, bank statements, and identity documents for higher extraction accuracy on those formats.

Document Classification

Automatically classifies incoming documents by type before routing them to the appropriate processor, supporting multi-document workflows.

Cloud API Integration

Exposes document processing capabilities through a REST and gRPC API on Google Cloud, with client libraries available for Python, Java, Node.js, and other languages.

Human Review Workflow

Integrates with Google Cloud's human-in-the-loop review tooling, allowing low-confidence extractions to be flagged and corrected by human reviewers before downstream use.

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Common questions about Google Document AI

What types of documents can Google Document AI process?

Google Document AI can process a broad range of document types, including general forms, invoices, receipts, contracts, identity documents, tax forms such as W-2s, bank statements, and pay stubs. Specialized processors are available for many of these categories, and a general-purpose OCR processor handles documents that do not fit a specific type.

Does Google Document AI have a context window or token limit?

The metadata for this model does not specify a context window or token limit. Google Document AI is a document extraction service rather than a generative language model, so its constraints are defined by document size and page count limits per API request rather than a token context window. Refer to the Google Cloud documentation for current per-request page limits.

How is Google Document AI priced?

Pricing information is not included in the available metadata. Google Document AI is a Google Cloud service billed based on the number of pages processed and the processor type used. Current pricing details are available on the Google Cloud Document AI pricing page.

Does Google Document AI require training on custom data?

No training is required to use the pre-built processors. Google Document AI also offers a Custom Document Extractor that allows users to train a processor on their own labeled documents when the pre-built processors do not meet their needs.

What output format does Google Document AI return?

The API returns results as structured JSON objects containing extracted text, field names and values, confidence scores, and bounding box coordinates for each detected element. This makes the output directly usable in downstream data pipelines without additional parsing.

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