Enterprise Document OCR

You can use Enterprise Document OCR as part of Document AI to detect and extract text and layout information from various documents. With configurable features, you can tailor the system to meet specific document-processing requirements.

Overview

You can use Enterprise Document OCR for tasks such as data entry based on algorithms or machine learning and improving and verifying data accuracy. You can also use Enterprise Document OCR to handle tasks like the following:

• Digitizing text: Extract text and layout data from documents for search, rules-based, document-processing pipelines, or custom-model creation.
• Using large language model applications: Use LLMs' contextual understanding and OCR's text and layout extraction capabilities to automate questions and answers. Unlock insights from data, and streamline workflows.
• Archiving: Digitize paper documents into machine-readable text to improve document accessibility.

Choosing the best OCR for your use case

Detection and extraction

Enterprise Document OCR can detect blocks, paragraphs, lines, words, and symbols from PDFs and images, as well as deskew documents for better accuracy.

Supported layout detection and extraction attributes:

Configurable Enterprise Document OCR features include the following:

• Extract embedded or native text from digital PDFs: This feature extracts text and symbols exactly as they appear in the source documents, even for rotated texts, extreme font sizes or styles, and partially hidden text.

• Rotation correction: Use Enterprise Document OCR to preprocess document images to correct rotation issues that can affect extraction quality or processing.

• Image-quality score: Receive quality metrics that can help with document routing. Image-quality score provides you with page-level quality metrics in eight dimensions, including blurriness, the presence of smaller-than-usual fonts, and glare.

• Specify page range: Specifies the range of the pages in an input document for OCR. This saves the spending and processing time over unneeded pages.

• Language detection: Detects the languages used in the extracted texts.

• Language and handwriting hints: Improve accuracy by providing the OCR model a language or handwriting hint based on the known characteristics of your dataset.

To learn how to enable OCR configurations, see Enable OCR configurations.

OCR add ons

Enterprise Document OCR offers optional analysis capabilities which can be enabled on individual processing requests as needed.

The following add-on capabilities are available for the Stable pretrained-ocr-v2.0-2023-06-02 and pretrained-ocr-v2.1-2024-08-07 versions, and Release Candidate pretrained-ocr-v2.1.1-2025-01-31 version.

• Math OCR: Identify and extract formulas from documents in LaTeX format.
• Checkbox extraction: Detect checkboxes and extract their status (marked/unmarked) in Enterprise Document OCR response.
• Font style detection: Identify word-level font properties including font type, font style, handwriting, weight, and color.

To learn how to enable the listed add-ons, see Enable OCR add ons.

Supported file formats

Enterprise Document OCR supports PDF, GIF, TIFF, JPEG, PNG, BMP, and WebP file formats. For more information, see Supported files.

Enterprise Document OCR also supports DocX files up to 15 pages in sync and 30 pages in async. To make a quota increase request (QIR), follow the steps to request a quota adjustment. DocX support is in private preview. To request access, contact your Google account team.

Advanced versioning

Advanced versioning is in Preview. Upgrades to the underlying AI/ML OCR models might lead to changes in OCR behavior. If strict consistency is required, use a frozen model version to pin behavior to a legacy OCR model for up to 18 months. This ensures the same image to OCR function result. See the table about processor versions.

Processor versions

The following processor versions are compatible with this feature. For more information, see Managing processor versions.

Use Enterprise Document OCR to process documents

This quickstart introduces you to Enterprise Document OCR. It shows you how to optimize document OCR results for your workflow by enabling or disabling any of the available OCR configurations.

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Document AI API.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Document AI API.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the API

Create an Enterprise Document OCR processor

First, create an Enterprise Document OCR processor. For more information, see creating and managing processors.

OCR configurations

All OCR configurations can be enabled by setting the respective fields in ProcessOptions.ocrConfig in the ProcessDocumentRequest or BatchProcessDocumentsRequest.

For more information, refer to Send a processing request.

Image-quality analysis

Intelligent document-quality analysis uses machine learning to perform quality assessment of a document based on the readability of its content. This quality assessment is returned as a quality score [0, 1], where 1 means perfect quality. If the quality score detected is lower than 0.5, a list of negative quality reasons (sorted by the likelihood) is also returned. Likelihood greater than 0.5 is considered a positive detection.

If the document is considered to be defective, the API returns the following eight document defect types:

• quality/defect_blurry
• quality/defect_noisy
• quality/defect_dark
• quality/defect_faint
• quality/defect_text_too_small
• quality/defect_document_cutoff
• quality/defect_text_cutoff
• quality/defect_glare

There are some limitations with the current document-quality analysis:

• It can return false positive detections with digital documents with no defects. The feature is best used on scanned or photographed documents.

• Glare defects are local. Their presence might not hinder overall document readability.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
        "enableImageQualityScores": true
      }
    }
  }

  {
    "pages": [
      {
        "imageQualityScores": {
          "qualityScore": 0.7811847,
          "detectedDefects": [
            {
              "type": "quality/defect_document_cutoff",
              "confidence": 1.0
            },
            {
              "type": "quality/defect_glare",
              "confidence": 0.97849524
            },
            {
              "type": "quality/defect_text_cutoff",
              "confidence": 0.5
            }
          ]
        }
      }
    ]
  }

Language hints

The OCR processor supports language hints that you define to improve OCR engine performance. Applying a language hint allows for OCR to optimize for a selected language instead of an inferred language.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
        "hints": {
          "languageHints": ["en", "es"]
        }
      }
    }
  }

Symbol detection

Populate data at the symbol (or individual letter) level in the document response.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
        "enableSymbol": true
      }
    }
  }

Built-in PDF parsing

Extract embedded text from digital PDF files. When enabled, if there is digital text, the built-in digital PDF model is automatically used. If there is non-digital text, the optical OCR model is automatically used. The user receives both text results merged together.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
        "enableNativePdfParsing": true
      }
    }
  }

Character-in-the-box detection

By default, Enterprise Document OCR has a detector enabled to improve text-extraction quality of characters that sit within a box. Here is an example:

If you're experiencing OCR quality issues with characters inside boxes, you can disable it.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
        "disableCharacterBoxesDetection": true
      }
    }
  }

Legacy layout

If you require a heuristics layout-detection algorithm, you can enable legacy layout, which serves as an alternative to the current ML-based, layout-detection algorithm. This is not the recommended configuration. Customers can choose the best suitable layout algorithm based on their document workflow.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
          "advancedOcrOptions": ["legacy_layout"]
      }
    }
  }

Specify a page range

By default, OCR extracts text and layout information from all pages in the documents. You can select specific page numbers or page ranges and only extract text from those pages.

There are three ways to configure this in ProcessOptions:

• To only process the second and fifth page:

  {
    "individualPageSelector": {"pages": [2, 5]}
  }

• To only process the first three pages:

  {
    "fromStart": 3
  }

• To only process the last four pages:

  {
    "fromEnd": 4
  }

In the response, each Document.pages[].pageNumber corresponds the same pages specified in the request.

OCR add ons uses

These Enterprise Document OCR optional analysis capabilities can be enabled on individual processing requests as needed.

Math OCR

Math OCR detects, recognizes, and extracts formulas, such as mathematical equations represented as LaTeX along with bounding box coordinates.

Here is an example of LaTeX representation:

• Image detected

  

• Conversion to LaTeX

  

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
          "premiumFeatures": {
            "enableMathOcr": true
          }
      }
    }
  }

"visualElements": [
  {
    "layout": {
      "textAnchor": {
        "textSegments": [
          {
            "endIndex": "46"
          }
        ]
      },
      "confidence": 1,
      "boundingPoly": {
        "normalizedVertices": [
          {
            "x": 0.14662756,
            "y": 0.27891156
          },
          {
            "x": 0.9032258,
            "y": 0.27891156
          },
          {
            "x": 0.9032258,
            "y": 0.8027211
          },
          {
            "x": 0.14662756,
            "y": 0.8027211
          }
        ]
      },
      "orientation": "PAGE_UP"
    },
    "type": "math_formula"
  }
]

Selection mark extraction

If enabled, the model attempts to extract all checkboxes and radio buttons in the document, along with bounding box coordinates.

  {
    "rawDocument": {
      "mimeType": "MIME_TYPE",
      "content": "IMAGE_CONTENT"
    },
    "processOptions": {
      "ocrConfig": {
          "premiumFeatures": {
            "enableSelectionMarkDetection": true
          }
      }
    }
  }

"visualElements": [
  {
    "layout": {
      "confidence":