This is the third post in our ‘OCR in Action’ series, where we continue our exploration of OCR and intelligent document processing across leading cloud platforms. In this edition, we’re turning our focus to Google Document AI, a strong contender in the IDP space known for its cloud-native design and advanced AI-driven document understanding.
Earlier in the series, we covered AWS Textract and Azure AI Document Intelligence.
Google Document AI: A Quick Look
Google Document AI is a unified, AI-powered platform that extracts structured data from documents using advanced Optical Character Recognition (OCR), layout understanding, and entity detection.
You get pre-trained models for common documents, plus the flexibility to build custom models tailored to your specific workflows. With fast processing and structured JSON output, Document AI helps automate document-heavy processes with high accuracy. Whether you have a small, immediate job or a big, company-wide process, it scales easily using both online (real-time) and batch (high-volume) processing modes.
OCR & General Processing Models
These are the fundamental tools in Document AI for general document work:
| Processor | Ideal For | Key Capabilities |
| Document OCR | General text extraction | Detects printed & handwritten text, supports multiple languages |
| Form Parser | Structured forms | Extracts text fields, checkboxes, and selection inputs |
| Layout Parser | Complex document structures | Identifies layout, paragraphs, tables, and reading order |
Google also provides a range of specialized pre-trained processors, including Invoice Parser, Receipt Parser, Contract Parser, Bank Statement Parser, and Identity Document Parser.
Processing Modes in Google Document AI
Before diving into the two modes, you need to know one thing: Google Document AI is a Google Cloud tool. This means documents must be stored locally or in Google Cloud Storage (GCS). If your files are sitting in AWS S3 or Azure Blob Storage, you’ll need to either load them into memory (as byte[]) or copy them into a GCS bucket first.
1. Online (Synchronous) Processing
Online processing is designed for real-time scenarios where results are needed immediately. You submit a single document, and Document AI processes it on the spot, returning a complete Document object containing the extracted text, layout information, and entities. This mode is ideal for interactive applications, quick validation, and low-volume workloads where instant feedback is essential.
Processing Options:
- Local Files: Read files from your application’s filesystem
- Google Cloud Storage: Process files directly from GCS without downloading
- Hybrid Approach: Read from external cloud storage (S3, Azure Blob) as byte[], or copy to GCS before processing
Typical Workflow:
2. Batch (Asynchronous) Processing
Batch processing is optimized for high-volume or automated document workflows. Instead of sending documents one by one, you can submit multiple files in a single batch request. Document AI returns a long-running operation that you can poll until processing completes. Once finished, the results along with BatchProcessMetadata are stored in a designated GCS bucket.
If the input documents are stored in a bucket owned by another project, appropriate access permissions must be granted before processing. Batch mode requires that all input documents be stored in Google Cloud Storage.
Typical Workflow:
How Google Document AI Works
Google Document AI operates through a straightforward yet powerful architecture:
Processing Pipeline
Getting Started with Google Document AI
To get started with Document AI, you will need the standard Google Cloud prerequisites:
- An active GCP account with billing enabled
(New users receive $300 in free credits; the free tier includes 1,000 pages per month.) - A Google Cloud Project with the Document AI API enabled
- A Processor created (Document OCR in this demonstration)
- A service account with the associated JSON key for authentication
- Documents to test both Online (Synchronous) and Batch (Asynchronous) processing modes
Creating Searchable PDFs
While Google Document AI excels at text extraction, it does not directly produce searchable PDFs. Instead, it returns structured JSON containing extracted text, bounding boxes, and layout metadata.
Typical Workflow:
- Process the document through Document AI
- Use open-source libraries to overlay transparent text on the original PDF
- Output a searchable PDF with the original visual layout preserved
What’s Next?
We’ve now explored AWS Textract, Azure AI Document Intelligence, and Google Document AI. Stay tuned for the next article, where we’ll bring all three together, compare their strengths and limitations, and give a clear analysis of which platform is the best fit for your specific solution.
Have questions or want to explore OCR solutions for your organization? Reach out to us. We’re always up for a good chat.
About the author : Divya Viswanath is a Application Architect at AOT Technologies with over five years of experience designing and building scalable digital solutions and modernizing enterprise applications for the public sector.