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Data Science AnalyticsTop 9 Best Scanning Documents Software of 2026
Top 10 Scanning Documents Software comparison ranks tools for document OCR, key extraction, and accuracy, including Azure Document Intelligence, Textract.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Microsoft Azure AI Document Intelligence
Custom form model training enables field-level schemas and organization-specific extraction for consistent document types.
Built for fits when teams need API-driven extraction for forms, tables, and key-value fields at controlled governance..
Google Cloud Document AI
Editor pickDocument AI processor pipelines that emit schema-aligned structured fields from scans for automated downstream ingestion.
Built for fits when teams require schema-controlled extraction integrated into Google Cloud pipelines..
Amazon Textract
Editor pickAnalyzeDocument returns structured form fields and table cells with bounding boxes and confidence scores in JSON.
Built for fits when AWS-centric teams need schema-driven OCR for forms and tables with controlled automation and governance..
Related reading
Comparison Table
This comparison table maps scanning documents software across integration depth, data model and schema design, and the automation and API surface used to turn images or PDFs into structured fields. It also summarizes admin and governance controls, including provisioning workflows, RBAC coverage, and audit log availability, so tradeoffs in extensibility and throughput are visible. Tools compared include major cloud document intelligence services and enterprise capture platforms such as Microsoft Azure AI Document Intelligence, Google Cloud Document AI, Amazon Textract, OpenText Capture Center, and Hyland OnBase.
Microsoft Azure AI Document Intelligence
API extractionDocument processing API that turns scanned pages into structured text, tables, and key-value fields with configurable extraction models and confidence metadata for downstream analytics pipelines.
Custom form model training enables field-level schemas and organization-specific extraction for consistent document types.
Azure AI Document Intelligence offers layout-aware analysis that can return structured fields, tables, and line-level text, which supports downstream schema mapping. The data model centers on document analysis results that include confidence signals and coordinate-level annotations that can drive validation and human review queues. Custom form models let teams define field schemas and train extraction on domain documents with consistent field layouts.
A concrete tradeoff is that accuracy depends on document quality and the degree of layout variability, so wide variation may require multiple models or additional training. It fits organizations that need repeatable extraction with an automation surface, such as document intake for accounts payable where tables and invoice line items must map into an internal schema. Governance and admin control work best when Azure RBAC, resource scoping, and audit logging are already in place for the broader AI and storage components.
- +Schema-driven form extraction with custom models
- +Layout and table outputs with structured coordinates
- +Production automation via REST API integration
- +Works with Azure RBAC and audit logging patterns
- –Layout variability may require multiple models
- –Field accuracy can drop with noisy scans and skew
Accounts payable automation teams
Extract invoices and line-item tables
Faster posting with fewer manual reviews
Claims operations teams
Normalize adjuster submitted documents
Consistent intake across document variants
Show 2 more scenarios
Document control teams
Index scanned policy documents
Better retrieval with fewer missed fields
Detect text and structured elements to populate searchable metadata with confidence checks.
Software engineering teams
Build extraction workflows for multiple schemas
Repeatable processing with automation
Orchestrate analyzers through REST calls and map results into versioned downstream data models.
Best for: Fits when teams need API-driven extraction for forms, tables, and key-value fields at controlled governance.
More related reading
Google Cloud Document AI
managed APIManaged document processing services that convert scanned inputs into structured outputs like entities, tables, and normalized fields with model customization and API-based integration.
Document AI processor pipelines that emit schema-aligned structured fields from scans for automated downstream ingestion.
Google Cloud Document AI accepts images and PDFs and returns structured outputs such as key-value fields and tables tied to a configurable schema. The service runs through a documented API surface that supports synchronous extraction and asynchronous batch jobs for throughput control. Integration depth is strongest when ingestion, storage, and workflows use Google Cloud primitives like Cloud Storage for inputs and Pub/Sub or Cloud workflows for orchestration.
A tradeoff appears in schema governance and operational overhead since extraction results are only useful when schemas and field mappings are maintained across document variants. A common usage situation is invoice and receipt ingestion where teams need consistent field extraction into a normalized schema and automated routing into ERP or bookkeeping pipelines.
- +API-driven extraction with synchronous and batch job patterns
- +Configurable schema mapping for fields, tables, and key-value output
- +Tight integration with Cloud Storage, Pub/Sub, and workflow orchestration
- +Operational controls for job management and throughput via batch execution
- –High schema maintenance effort for frequent document format changes
- –Output normalization requires additional logic for cross-template consistency
Accounts payable teams
Invoice batch extraction into ERP
Reduced manual invoice data entry
Compliance operations
KYC document field verification
Faster compliance document screening
Show 2 more scenarios
Revenue operations teams
Sales contract metadata extraction
Improved CRM searchability
Converts signed contracts and proposals into key-value and table fields for CRM indexing.
Logistics teams
Bill of lading data capture
Lower errors in shipment records
Extracts shipment details from PDFs and routes structured outputs to tracking and dispatch systems.
Best for: Fits when teams require schema-controlled extraction integrated into Google Cloud pipelines.
Amazon Textract
serverless APIExtract structured data from scanned documents through API operations for forms and tables, with confidence scores and line-level text for programmatic ingestion.
AnalyzeDocument returns structured form fields and table cells with bounding boxes and confidence scores in JSON.
Amazon Textract provides DetectDocumentText for unstructured OCR and AnalyzeDocument for forms and tables extracted into a machine-readable JSON schema. The data model includes confidence scores, bounding boxes, and normalized field results that make it practical to validate outputs before persistence. Automation is centered on a well-defined API surface and asynchronous batch processing that works with large file volumes. Provisioning and governance align with AWS account controls via IAM policies and resource scoping.
A key tradeoff is that accurate schema mapping depends on document layout consistency, so highly variable templates may require custom workflows and post-processing. Amazon Textract fits best when document capture is already flowing into AWS storage and processing layers, with outputs routed to indexing, reconciliation, or case management systems. A common usage situation is extracting invoice line-item tables and key fields from PDFs and images, then applying validation rules and audit logging downstream.
- +Managed OCR for text, forms, and tables with JSON structure
- +Asynchronous batch jobs support high throughput document processing
- +AWS IAM alignment enables scoped access and operational governance
- +Bounding boxes and confidence scores support validation before storage
- –Template variability can reduce form and table field reliability
- –Downstream schema normalization and validation often still require custom code
Accounts payable operations teams
Extract invoice fields from scanned PDFs
Reduced manual invoice data entry
Workflow automation engineers
Trigger document OCR with event processing
Faster turnaround for cases
Show 2 more scenarios
Information security governance teams
Enforce RBAC on document processing
Controlled access to OCR data
IAM policies scope access to processing calls and storage locations used by OCR jobs.
Data platform teams
Index OCR results for search
Higher precision in document search
Confidence and geometry metadata enable quality gates before committing to an index schema.
Best for: Fits when AWS-centric teams need schema-driven OCR for forms and tables with controlled automation and governance.
OpenText Capture Center
enterprise captureDocument capture solution that supports scan ingest, batch processing, extraction workflows, and governed export of captured fields to enterprise content and records systems.
Schema-driven capture data model with extensible workflow automation that maps extracted fields to downstream ingestion targets.
OpenText Capture Center targets document scanning workflows that need integration depth, not just image capture. It focuses on configuring a structured capture data model, then driving automation through workflow configuration and extension points.
Admin controls center on provisioning of capture components, role-based access for operational users, and auditability of processing actions. Integration is shaped around an automation and API surface used to connect capture outputs to downstream systems for indexing, routing, and record creation.
- +Configurable data model for consistent indexing across document types
- +Integration-oriented automation points for routing captured fields
- +API and extensibility support for connecting external processing services
- +Admin provisioning and role-based access control for operational governance
- +Audit log coverage for tracking document processing actions
- –Workflow configuration can be complex for teams without schema ownership
- –Automation depth depends on having a stable downstream data contract
- –RBAC boundaries require careful role mapping during onboarding
- –Throughput tuning often needs coordinated changes across capture and indexing
Best for: Fits when enterprises need schema-driven capture automation with governance and integration to content and record systems.
OnBase
content captureEnterprise information capture system that combines document capture, indexing, and workflow controls to store scanned artifacts and extracted data with administrative governance features.
OnBase content data model with configured document types, indexes, and retention policies.
OnBase captures and processes scanned documents into a governed content and workflow environment with configurable schemas and retention. It integrates scanning, indexing, OCR, and document routing into a shared data model used by business applications.
Automation is driven through workflow configuration plus API-driven services that connect ingestion and downstream case processing. Administrative controls focus on RBAC, audit logging, and provisioning rules that define who can scan, classify, view, and act on documents.
- +Configurable document types and indexing schemas reduce rework during capture
- +Workflow routing connects scanned artifacts to case stages and worklists
- +API surface supports integration between capture, services, and downstream apps
- +RBAC and audit logs support governance for scan and document lifecycle actions
- –Extensive configuration increases implementation effort and change-management overhead
- –OCR quality depends on input quality and configured recognition settings
- –High customization can make schema and workflow upgrades harder to plan
- –Throughput and scanner fleet behavior depend on careful deployment tuning
Best for: Fits when regulated teams need governed document capture with schema control and workflow-driven routing.
Nanonets
template extractionAI-assisted document OCR and form extraction with configurable document templates, rule-based validation, and API access to manage documents and extracted fields.
Document field extraction driven by configurable schema, exposed through an API for programmable workflows.
Nanonets fits teams that need document scanning plus structured extraction with automation and controlled access. The core is a configurable data model for fields and schemas, paired with an API surface for upload, processing status, and results retrieval.
Automation centers on workflow triggers from ingestion to extraction outputs, with extensibility for connecting downstream systems. Governance relies on account-level administration, RBAC-style permissioning, and audit log visibility for operational traceability.
- +Configurable extraction schema reduces custom parsing and mapping work
- +API supports ingestion, job polling, and results retrieval for automation
- +Automation hooks connect scan outputs to downstream workflows
- +Admin controls include role-based access for operational separation
- +Audit log visibility supports traceability for processed documents
- –Schema changes can require careful versioning across workflows
- –Throughput depends on job sizing and async processing patterns
- –Governance granularity may require extra setup for complex orgs
- –Model configuration requires schema discipline to avoid field drift
Best for: Fits when teams need scan-to-structured extraction with API-driven automation and enforceable access control.
Docparser
schema extractionDocument parsing platform that maps uploaded scans or PDFs into extracted fields using document layouts and rules, with API delivery of structured outputs.
Schema plus API and webhooks for automated field extraction and ingestion into downstream systems.
Docparser converts document images and PDFs into structured fields using OCR and extraction rules defined in a schema. It differentiates by exposing an API and webhook workflow that supports automated submission and downstream processing.
Automation centers on configurable templates, field mapping, and extensibility through programmatic endpoints rather than manual exports. Admin controls focus on organization setup, access scoping, and operational visibility such as audit trails for governance.
- +API and webhooks connect extraction runs to external systems
- +Schema-driven extraction keeps field definitions consistent across documents
- +Template configuration reduces per-document manual mapping effort
- +Extensible endpoints support custom processing pipelines
- –Template governance requires careful versioning for changing document layouts
- –Complex layouts can increase extraction tuning and validation overhead
- –Throughput and job control are less granular than queue-native systems
- –RBAC granularity may be limited for multi-team document workloads
Best for: Fits when mid-size teams need schema-based document extraction with API automation.
Rossum
AI extractionDocument AI for invoice and back-office document extraction that uses configurable parsing, human-in-the-loop review, and API access for extracted fields.
Schema and training workflow for field-level extraction using configured document types.
Rossum is an automated document scanning system focused on extracting structured fields from invoices, purchase orders, and other document types. Its data model centers on document schemas and training examples that map extracted values to configured fields.
Automation and extensibility are handled through APIs for document ingest, processing, and workflow actions. Integration depth is expressed through configurable connectors, webhook-style triggers, and role-based controls for managing who can configure schemas and review outputs.
- +Schema-driven extraction maps documents to explicit field definitions
- +API supports document ingest, processing, and extraction lifecycle actions
- +Webhook-style automation enables event-driven downstream handling
- +RBAC separates schema configuration, review access, and operational permissions
- +Audit trails support governance over review and changes
- –Schema design requires upfront configuration to reach reliable accuracy
- –Complex multi-document workflows need custom orchestration via API
- –Throughput depends on model readiness and validation workload
Best for: Fits when document-heavy teams need schema-based extraction with API automation and RBAC governance for operations review.
Scanbot SDK
SDK scanningOn-device scanning SDK that captures document images and performs OCR with developer APIs so captured scans and extracted text can be streamed into custom systems.
Document quality validation and correction settings exposed through the scan result pipeline.
Scanbot SDK embeds document scanning into a custom iOS, Android, and web capture flow via an API. It centers on on-device image processing, edge detection, perspective correction, and document quality checks exposed through configurable processing options.
Scanbot SDK also provides capture-to-result pipelines that return structured scan outputs suited for persistence and downstream automation. Admin-grade control is mostly delivered through how teams provision and govern the SDK configuration, since the integration model is application-owned rather than centralized capture administration.
- +SDK integration supports custom capture flows on iOS and Android
- +Configurable image processing exposes clear control over correction and validation
- +Returns structured scan outputs that map to app data models
- +API surface supports automation of capture, processing, and result handling
- –Centralized admin console and org governance controls are not the primary model
- –Teams must own the data schema design and storage pipeline
- –Automation depth depends on custom integration rather than ready workflows
- –Throughput and scaling behavior rely on application-side orchestration
Best for: Fits when teams need an API-driven scanning pipeline inside an existing app with strong configuration control.
How to Choose the Right Scanning Documents Software
This buyer's guide covers nine scanning and document extraction tools, including Microsoft Azure AI Document Intelligence, Google Cloud Document AI, Amazon Textract, OpenText Capture Center, OnBase, Nanonets, Docparser, Rossum, and Scanbot SDK.
Each tool is assessed for integration depth, data model design, automation and API surface, and admin and governance controls so document ingestion can connect directly to downstream systems with controlled access.
Document-to-structure capture systems that turn scans into governed fields for downstream workflows
Scanning Documents Software converts captured images or PDFs into structured outputs like text, tables, and key-value fields and then routes those outputs into indexing, case management, or analytics pipelines. Microsoft Azure AI Document Intelligence focuses on schema-driven extraction for forms, tables, and key-value fields using REST calls and configurable extraction models that emit structured results.
Google Cloud Document AI emphasizes document processor pipelines that emit schema-aligned structured fields for automated downstream ingestion through synchronous and batch API patterns.
Teams typically use these systems when document layouts vary enough to require layout-aware extraction, but standardized downstream processing needs a stable schema, validation path, and audit trail.
Integration, schema control, automation surface, and governance mechanics
Selection should start with the data model because extraction quality and downstream mapping depend on schema alignment, not just OCR accuracy. OpenText Capture Center and OnBase build around a configurable capture or content data model that supports consistent indexing across document types.
Automation and API surface determines how quickly scanning events become structured records, routing actions, or workflow transitions. Microsoft Azure AI Document Intelligence and Amazon Textract both expose production automation through REST or JSON-based extraction that includes confidence scores and structured coordinates for programmatic validation.
Admin and governance controls determine whether teams can safely scale extraction across users and workflows using provisioning, RBAC, and audit log coverage.
Schema-driven extraction with configurable field models
Microsoft Azure AI Document Intelligence supports custom form model training that maps organization-specific fields into explicit schemas for consistent extraction. OpenText Capture Center also uses a schema-driven capture data model so extracted fields map directly to downstream ingestion targets with less ad hoc parsing.
Processor output that preserves layout context for tables and coordinates
Azure AI Document Intelligence returns layout and table outputs with structured coordinates, which helps downstream systems reproduce table structure reliably. Amazon Textract returns form fields and table cells in JSON with bounding boxes so validation can happen before storing extracted data.
Document AI integration depth into existing cloud or enterprise systems
Google Cloud Document AI integrates tightly with Google Cloud services and supports workflow orchestration with batch job patterns. OnBase and OpenText Capture Center integrate scanning into enterprise content, records, and workflow environments using a shared data model for indexing and routing.
Automation and API patterns for ingestion, batch processing, and event-driven workflows
Amazon Textract supports asynchronous batch jobs for high throughput and also fits event-driven API calls for programmatic ingestion. Docparser provides an API and webhooks so extraction runs can trigger downstream ingestion actions without manual exports.
Confidence and validation metadata for safer downstream ingestion
Amazon Textract emits confidence scores and bounding boxes so applications can validate results before storage. Azure AI Document Intelligence outputs confidence metadata for downstream analytics pipelines, which supports gating logic when scans are noisy or skewed.
Admin provisioning, RBAC, and audit trail coverage for processing governance
Azure AI Document Intelligence works with Azure RBAC and aligns with audit logging patterns, which supports controlled access for extraction and analytics. OpenText Capture Center and OnBase provide role-based access and audit log coverage that tracks processing actions across capture and workflow operations.
A selection framework for schema control, automation depth, and governed access
Start with the data model shape needed for downstream systems because form fields, tables, and key-value pairs map to different schemas and validation rules. If downstream processing requires field-level schemas for consistent extraction across repeated document types, Microsoft Azure AI Document Intelligence and Nanonets both center schema configuration and extraction outputs around explicit fields.
Next, verify that the automation and API surface matches ingestion and throughput needs. Amazon Textract supports both batch and programmatic ingestion with JSON outputs, while Docparser uses webhooks plus API endpoints for automated submission and downstream processing.
Finally, confirm governance mechanics like RBAC boundaries and audit log coverage so document processing actions can be tracked and controlled across teams.
Map your downstream contract to a tool’s data model and schema control
Define which extracted elements must be stable, like invoice line items, purchase order fields, or key-value pairs. Microsoft Azure AI Document Intelligence and Rossum provide schema-driven extraction tied to configured fields, while Google Cloud Document AI and Docparser map extracted results to configured schemas for ingestion.
Verify table and layout fidelity using coordinates and structured outputs
If downstream systems reconstruct tables, choose tools that emit table structure and layout context. Azure AI Document Intelligence returns layout and table outputs with structured coordinates, while Amazon Textract emits bounding boxes and JSON for table cells and form fields.
Match throughput needs to batch or event-driven automation patterns
For high-volume processing, validate that the tool supports batch job patterns and asynchronous execution. Google Cloud Document AI and Amazon Textract both support batch job patterns for high-throughput document processing, while Docparser and Rossum support API and webhook-style automation for event-driven downstream handling.
Plan governance through RBAC, provisioning, and audit trail coverage
Confirm whether governance is handled through platform controls, enterprise capture controls, or both. Azure AI Document Intelligence aligns with Azure RBAC and audit logging patterns, while OpenText Capture Center and OnBase provide provisioning, RBAC for operational users, and audit log coverage for processing actions.
Assess schema maintenance effort against real document variability
If document formats change frequently, schema maintenance becomes a measurable operational cost. Google Cloud Document AI can require high schema maintenance effort when document formats shift, while Azure AI Document Intelligence may need multiple models when layout variability requires different extraction approaches.
Audience fit by integration depth, automation expectations, and governance requirements
Different scanning teams want different control surfaces, from cloud-native API extraction to enterprise capture governance. The best fit depends on whether extraction needs to be schema-governed and routed into enterprise content and records systems or embedded into an existing application experience.
Tools also differ in where they place admin responsibility, which affects how RBAC and audit log requirements get implemented across teams.
Teams building schema-governed document extraction pipelines in Azure
Microsoft Azure AI Document Intelligence fits teams needing API-driven extraction for forms, tables, and key-value fields under Azure RBAC and audit logging patterns. Custom form model training supports field-level schemas that stay consistent across repeated document types.
Teams standardizing extraction inside Google Cloud workloads
Google Cloud Document AI fits organizations that want schema-controlled extraction integrated with Cloud Storage, Pub/Sub, and workflow orchestration. Processor pipelines emit schema-aligned structured fields for automated downstream ingestion.
AWS-centric teams that need JSON outputs for forms and tables
Amazon Textract fits AWS-centric teams that need AnalyzeDocument outputs with bounding boxes and confidence scores in JSON. Asynchronous batch jobs support higher throughput when document volume spikes.
Enterprises that require governed capture, indexing, and record routing
OpenText Capture Center and OnBase fit regulated enterprises that need a schema-driven capture or content data model plus RBAC and audit log coverage. Workflow routing and extensible integration points map extracted fields to downstream indexing, routing, and record creation.
Teams embedding scanning and quality checks into their own mobile or web apps
Scanbot SDK fits teams that need an on-device capture and OCR SDK with configurable document quality validation and correction exposed through developer APIs. Governance is mainly achieved by how the application provisions and configures the SDK rather than centralized capture administration.
Where document scanning programs fail in integration, schema, and governance
Many scanning programs fail when extraction outputs are treated as interchangeable OCR text instead of governed structured fields. Field-level accuracy drops with noisy scans and skew in Azure AI Document Intelligence, which makes validation logic and confidence handling necessary rather than optional.
Schema design and governance also get underestimated when document layouts vary across templates. Google Cloud Document AI can require high schema maintenance effort for frequent format changes, and Docparser template governance requires careful versioning for changing document layouts.
Treating OCR results as final without validation metadata
Downstream systems should use bounding boxes, confidence scores, and structured coordinates from tools like Amazon Textract and Microsoft Azure AI Document Intelligence instead of accepting raw extracted text. Confidence metadata supports gating logic when field accuracy drops on skewed or noisy scans.
Underestimating schema maintenance and template versioning effort
Frequent document layout changes create operational cost in Google Cloud Document AI and Docparser because schema or template updates must stay aligned across workflows. Microsoft Azure AI Document Intelligence can also require multiple models when layout variability needs different extraction approaches.
Relying on workflow configuration without stable downstream data contracts
OpenText Capture Center and OnBase require coordinated changes across capture and indexing because throughput tuning depends on both sides of the data contract. Without a stable extraction-to-index contract, automation breaks into manual mapping work.
Choosing an integration model that does not match where governance must live
Scanbot SDK places centralized admin and org governance outside the primary model, which pushes governance responsibility into the application. OpenText Capture Center and OnBase provide stronger enterprise governance through provisioning, RBAC, and audit log coverage for operational users.
How We Evaluated and Ranked These Document Scanning Tools
We evaluated Microsoft Azure AI Document Intelligence, Google Cloud Document AI, Amazon Textract, OpenText Capture Center, OnBase, Nanonets, Docparser, Rossum, and Scanbot SDK using features, ease of use, and value ratings tied to concrete capabilities like schema-driven extraction and API automation patterns. We rated each tool on how well its integration depth, data model design, and automation surface support production workflows, then we combined those signals into an overall rating where features carry the most weight at 40%. Ease of use and value each account for 30% of the overall rating based on how the product mechanics support implementation and operational handoffs.
Microsoft Azure AI Document Intelligence stood apart because schema-driven custom form model training enables field-level schemas for consistent extraction, and that strength directly lifts both the features score and the practicality of governed integration patterns via REST calls, Azure RBAC alignment, and confidence metadata for downstream analytics pipelines.
Frequently Asked Questions About Scanning Documents Software
How do Microsoft Azure AI Document Intelligence and Google Cloud Document AI handle schema-driven extraction?
What is the tradeoff between Amazon Textract JSON output and OpenText Capture Center’s capture data model?
Which tools support API and webhook workflows for automated document ingestion and extraction?
How do OnBase and OpenText Capture Center differ in admin controls for operational teams?
What security and access controls are commonly required for regulated teams, and which tools map well?
How does data migration typically work when replacing one extraction workflow with another?
Which tools provide extensibility through connectors, workflow configuration, or post-processing hooks?
What throughput and batch-processing patterns are supported by the major cloud OCR platforms?
When should teams use Scanbot SDK instead of server-side document AI platforms like Textract or Document AI?
Conclusion
After evaluating 9 data science analytics, Microsoft Azure AI Document Intelligence stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
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