You can use Enterprise Document OCR as part of Document AI to detect andextract text and layout information from various documents. With configurablefeatures, you can tailor the system to meet specific document-processingrequirements.

Overview

You can use Enterprise Document OCR for tasks such as data entry based onalgorithms or machine learning and improving and verifying data accuracy. Youcan 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 understandingand OCR's text and layout extraction capabilities to automate questions andanswers. Unlock insights from data, and streamline workflows.
• Archiving: Digitize paper documents into machine-readable text to improvedocument accessibility.

Choosing the best OCR for your use case

Detection and extraction

Enterprise Document OCR can detect blocks, paragraphs, lines, words, andsymbols 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 extractstext and symbols exactly as they appear in the source documents, even forrotated texts, extreme font sizes or styles, and partially hidden text.

• Rotation correction: Use Enterprise Document OCR to preprocessdocument images to correct rotation issues that can affect extraction quality orprocessing.

• Image-quality score: Receive quality metrics that can help with documentrouting. Image-quality score provides you with page-level quality metrics ineight dimensions, including blurriness, the presence of smaller-than-usualfonts, and glare.

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

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

• Language and handwriting hints: Improve accuracy by providing the OCRmodel a language or handwriting hint based on the known characteristics of yourdataset.

To learn how to enable OCR configurations, seeEnable OCR configurations.

OCR add ons

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

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

• Math OCR: Identify and extract formulas from documents inLaTeX 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 fonttype, font style, handwriting, weight, and color.

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

Supported file formats

Enterprise Document OCR supports PDF, GIF, TIFF, JPEG, PNG, BMP, and WebPfile formats. For more information, seeSupported files.

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

Advanced versioning

Advanced versioning is inPreview. Upgradesto the underlying AI/ML OCR models might lead to changes in OCR behavior. Ifstrict consistency is required, use afrozen model version to pin behavior toa legacy OCR model for up to 18 months. This ensures the same image to OCRfunction result. See the table aboutprocessor versions.

Processor versions

The following processor versions are compatible with this feature. For moreinformation, seeManaging processor versions.

Use Enterprise Document OCR to process documents

This quickstart introduces you to Enterprise Document OCR. It shows you how tooptimize document OCR results for your workflow by enabling or disabling any ofthe 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 theresourcemanager.projects.create permission.Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

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 Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.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 theresourcemanager.projects.create permission.Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

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 Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the API

Create an Enterprise Document OCR processor

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

OCR configurations

All OCR configurations can be enabled by setting the respective fields inProcessOptions.ocrConfig in theProcessDocumentRequest orBatchProcessDocumentsRequest.

For more information, refer toSend 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], where1 means perfect quality.If the quality score detected is lower than0.5, a list of negative quality reasons (sorted by the likelihood) is also returned.Likelihood greater than0.5 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 inProcessOptions:

• To only process the second and fifth page:

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

{"fromStart":3}

{"fromEnd":4}

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

OCR add ons uses

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

Math OCR

Math OCR detects, recognizes, and extracts formulas, such as mathematical equations represented asLaTeX 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":0.89363575,"boundingPoly":{"vertices":[{"x":11,"y":24},{"x":37,"y":24},{"x":37,"y":56},{"x":11,"y":56}],"normalizedVertices":[{"x":0.017488075,"y":0.38709676},{"x":0.05882353,"y":0.38709676},{"x":0.05882353,"y":0.9032258},{"x":0.017488075,"y":0.9032258}]}},"type":"unfilled_checkbox"},{"layout":{"confidence":0.9148201,"boundingPoly":...},"type":"filled_checkbox"}],

Font-style detection

With font-style detection enabled, Enterprise Document OCR extracts font attributes, which can be used for better post-processing.

At the token (word) level, the following attributes are detected:

• Handwriting detection
• Font style
• Font size
• Font type
• Font color
• Font weight
• Letter spacing
• Bold
• Italic
• Underlined
• Text color (RGBa)

• Background color (RGBa)

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

"tokens":[{"styleInfo":{"fontSize":3,"pixelFontSize":13,"fontType":"SANS_SERIF","bold":true,"fontWeight":564,"textColor":{"red":0.16862746,"green":0.16862746,"blue":0.16862746},"backgroundColor":{"red":0.98039216,"green":0.9882353,"blue":0.99215686}}},...]

Convert document objects to Vision AI API format

TheDocument AI Toolbox includes a tool that converts the Document AI APIDocument format to the Vision AIAnnotateFileResponse format, enabling users to compare the responses between the document OCR processor and Vision AI API. Here is somesample code.

Known discrepancies between the Vision AI API response and Document AI API response and converter:

• The Vision AI API response populates onlyvertices for image requests, and populates onlynormalized_vertices for PDF requests. The Document AI response and the converter populates bothvertices andnormalized_vertices.
• The Vision AI API response populates thedetected_break in the last symbol of the word. The Document AI API response and the converter populatesdetected_break in the word and the last symbol of the word.
• The Vision AI API response always populates symbols fields. By default, the Document AI response does not populate symbols fields. To make sure the Document AI response and the converter get symbols fields populated, set theenable_symbol feature as detailed.

Code samples

The following code samples demonstrate how to send a processing request enabling OCR configurations and add ons, then read and print the fields to the terminal:

POST https://LOCATION-documentai.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/processors/PROCESSOR_ID/processorVersions/PROCESSOR_VERSION:process

{  "skipHumanReview":skipHumanReview,  "rawDocument": {    "mimeType": "MIME_TYPE",    "content": "IMAGE_CONTENT"  },  "fieldMask": "FIELD_MASK",  "processOptions": {    "ocrConfig": {      "enableNativePdfParsing":ENABLE_NATIVE_PDF_PARSING,      "enableImageQualityScores":ENABLE_IMAGE_QUALITY_SCORES,      "enableSymbol":ENABLE_SYMBOL,      "disableCharacterBoxesDetection":DISABLE_CHARACTER_BOXES_DETECTION,      "hints": {        "languageHints": [          "LANGUAGE_HINTS"        ]      },      "advancedOcrOptions": ["ADVANCED_OCR_OPTIONS"],      "premiumFeatures": {        "enableSelectionMarkDetection":ENABLE_SELECTION_MARK_DETECTION,        "computeStyleInfo":COMPUTE_STYLE_INFO,        "enableMathOcr":ENABLE_MATH_OCR,      }    },    "individualPageSelector" {      "pages": [INDIVIDUAL_PAGES]    }  }}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-documentai.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/processors/PROCESSOR_ID/processorVersions/PROCESSOR_VERSION:process"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-documentai.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/processors/PROCESSOR_ID/processorVersions/PROCESSOR_VERSION:process" | Select-Object -Expand Content

fromtypingimportOptional,Sequencefromgoogle.api_core.client_optionsimportClientOptionsfromgoogle.cloudimportdocumentai# TODO(developer): Uncomment these variables before running the sample.# project_id = "YOUR_PROJECT_ID"# location = "YOUR_PROCESSOR_LOCATION" # Format is "us" or "eu"# processor_id = "YOUR_PROCESSOR_ID" # Create processor before running sample# processor_version = "rc" # Refer to https://cloud.google.com/document-ai/docs/manage-processor-versions for more information# file_path = "/path/to/local/pdf"# mime_type = "application/pdf" # Refer to https://cloud.google.com/document-ai/docs/file-types for supported file typesdefprocess_document_ocr_sample(project_id:str,location:str,processor_id:str,processor_version:str,file_path:str,mime_type:str,)->None:# Optional: Additional configurations for Document OCR Processor.# For more information: https://cloud.google.com/document-ai/docs/enterprise-document-ocrprocess_options=documentai.ProcessOptions(ocr_config=documentai.OcrConfig(enable_native_pdf_parsing=True,enable_image_quality_scores=True,enable_symbol=True,# OCR Add Ons https://cloud.google.com/document-ai/docs/ocr-add-onspremium_features=documentai.OcrConfig.PremiumFeatures(compute_style_info=True,enable_math_ocr=False,# Enable to use Math OCR Modelenable_selection_mark_detection=True,),))# Online processing request to Document AIdocument=process_document(project_id,location,processor_id,processor_version,file_path,mime_type,process_options=process_options,)text=document.textprint(f"Full document text:{text}\n")print(f"There are{len(document.pages)} page(s) in this document.\n")forpageindocument.pages:print(f"Page{page.page_number}:")print_page_dimensions(page.dimension)print_detected_languages(page.detected_languages)print_blocks(page.blocks,text)print_paragraphs(page.paragraphs,text)print_lines(page.lines,text)print_tokens(page.tokens,text)ifpage.symbols:print_symbols(page.symbols,text)ifpage.image_quality_scores:print_image_quality_scores(page.image_quality_scores)ifpage.visual_elements:print_visual_elements(page.visual_elements,text)defprint_page_dimensions(dimension:documentai.Document.Page.Dimension)->None:print(f"    Width:{str(dimension.width)}")print(f"    Height:{str(dimension.height)}")defprint_detected_languages(detected_languages:Sequence[documentai.Document.Page.DetectedLanguage],)->None:print("    Detected languages:")forlangindetected_languages:print(f"{lang.language_code} ({lang.confidence:.1%} confidence)")defprint_blocks(blocks:Sequence[documentai.Document.Page.Block],text:str)->None:print(f"{len(blocks)} blocks detected:")first_block_text=layout_to_text(blocks[0].layout,text)print(f"        First text block:{repr(first_block_text)}")last_block_text=layout_to_text(blocks[-1].layout,text)print(f"        Last text block:{repr(last_block_text)}")defprint_paragraphs(paragraphs:Sequence[documentai.Document.Page.Paragraph],text:str)->None:print(f"{len(paragraphs)} paragraphs detected:")first_paragraph_text=layout_to_text(paragraphs[0].layout,text)print(f"        First paragraph text:{repr(first_paragraph_text)}")last_paragraph_text=layout_to_text(paragraphs[-1].layout,text)print(f"        Last paragraph text:{repr(last_paragraph_text)}")defprint_lines(lines:Sequence[documentai.Document.Page.Line],text:str)->None:print(f"{len(lines)} lines detected:")first_line_text=layout_to_text(lines[0].layout,text)print(f"        First line text:{repr(first_line_text)}")last_line_text=layout_to_text(lines[-1].layout,text)print(f"        Last line text:{repr(last_line_text)}")defprint_tokens(tokens:Sequence[documentai.Document.Page.Token],text:str)->None:print(f"{len(tokens)} tokens detected:")first_token_text=layout_to_text(tokens[0].layout,text)first_token_break_type=tokens[0].detected_break.type_.nameprint(f"        First token text:{repr(first_token_text)}")print(f"        First token break type:{repr(first_token_break_type)}")iftokens[0].style_info:print_style_info(tokens[0].style_info)last_token_text=layout_to_text(tokens[-1].layout,text)last_token_break_type=tokens[-1].detected_break.type_.nameprint(f"        Last token text:{repr(last_token_text)}")print(f"        Last token break type:{repr(last_token_break_type)}")iftokens[-1].style_info:print_style_info(tokens[-1].style_info)defprint_symbols(symbols:Sequence[documentai.Document.Page.Symbol],text:str)->None:print(f"{len(symbols)} symbols detected:")first_symbol_text=layout_to_text(symbols[0].layout,text)print(f"        First symbol text:{repr(first_symbol_text)}")last_symbol_text=layout_to_text(symbols[-1].layout,text)print(f"        Last symbol text:{repr(last_symbol_text)}")defprint_image_quality_scores(image_quality_scores:documentai.Document.Page.ImageQualityScores,)->None:print(f"    Quality score:{image_quality_scores.quality_score:.1%}")print("    Detected defects:")fordetected_defectinimage_quality_scores.detected_defects:print(f"{detected_defect.type_}:{detected_defect.confidence:.1%}")defprint_style_info(style_info:documentai.Document.Page.Token.StyleInfo)->None:"""    Only supported in version `pretrained-ocr-v2.0-2023-06-02`    """print(f"           Font Size:{style_info.font_size}pt")print(f"           Font Type:{style_info.font_type}")print(f"           Bold:{style_info.bold}")print(f"           Italic:{style_info.italic}")print(f"           Underlined:{style_info.underlined}")print(f"           Handwritten:{style_info.handwritten}")print(f"           Text Color (RGBa):{style_info.text_color.red},{style_info.text_color.green},{style_info.text_color.blue},{style_info.text_color.alpha}")defprint_visual_elements(visual_elements:Sequence[documentai.Document.Page.VisualElement],text:str)->None:"""    Only supported in version `pretrained-ocr-v2.0-2023-06-02`    """checkboxes=[xforxinvisual_elementsif"checkbox"inx.type]math_symbols=[xforxinvisual_elementsifx.type=="math_formula"]ifcheckboxes:print(f"{len(checkboxes)} checkboxes detected:")print(f"        First checkbox:{repr(checkboxes[0].type)}")print(f"        Last checkbox:{repr(checkboxes[-1].type)}")ifmath_symbols:print(f"{len(math_symbols)} math symbols detected:")first_math_symbol_text=layout_to_text(math_symbols[0].layout,text)print(f"        First math symbol:{repr(first_math_symbol_text)}")defprocess_document(project_id:str,location:str,processor_id:str,processor_version:str,file_path:str,mime_type:str,process_options:Optional[documentai.ProcessOptions]=None,)->documentai.Document:# You must set the `api_endpoint` if you use a location other than "us".client=documentai.DocumentProcessorServiceClient(client_options=ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com"))# The full resource name of the processor version, e.g.:# `projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}`# You must create a processor before running this sample.name=client.processor_version_path(project_id,location,processor_id,processor_version)# Read the file into memorywithopen(file_path,"rb")asimage:image_content=image.read()# Configure the process requestrequest=documentai.ProcessRequest(name=name,raw_document=documentai.RawDocument(content=image_content,mime_type=mime_type),# Only supported for Document OCR processorprocess_options=process_options,)result=client.process_document(request=request)# For a full list of `Document` object attributes, reference this page:# https://cloud.google.com/document-ai/docs/reference/rest/v1/Documentreturnresult.documentdeflayout_to_text(layout:documentai.Document.Page.Layout,text:str)->str:"""    Document AI identifies text in different parts of the document by their    offsets in the entirety of the document"s text. This function converts    offsets to a string.    """# If a text segment spans several lines, it will# be stored in different text segments.return"".join(text[int(segment.start_index):int(segment.end_index)]forsegmentinlayout.text_anchor.text_segments)

What's next

• Review theprocessors list.
• Separate documents into readable chunks withLayout Parser.
• Create acustom classifier.