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Data Science AnalyticsTop 10 Best Scanning Solutions Software of 2026
Top 10 Scanning Solutions Software ranking covers CamScanner, Adobe Acrobat, and Google Drive with criteria for teams comparing scan features and costs.
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.
CamScanner
On-device scan enhancement with perspective correction for clearer multipage PDF outputs.
Built for fits when teams need quick scanned PDFs for review and sharing without heavy admin automation..
Adobe Acrobat
Editor pickBuilt-in OCR for scanned pages that embeds searchable text into the resulting PDF.
Built for fits when teams need searchable PDFs plus review and redaction before system handoff..
Google Drive
Editor pickShared Drives with RBAC-based membership and role inheritance across files and folders.
Built for fits when governed storage and Drive API automation matter more than built-in scan capture orchestration..
Related reading
Comparison Table
This comparison table maps scanning and OCR tools by integration depth, including document ingestion paths, storage targets, and how each platform models extracted text and metadata. It also contrasts automation and API surface, such as OCR endpoints, webhook or batch options, and extensibility points for custom pipelines. The table adds admin and governance controls coverage, including RBAC, provisioning workflows, and audit log behavior.
CamScanner
mobile OCRMobile document scanning app with OCR text capture, multi-page PDF output, and export flows for local files and cloud destinations.
On-device scan enhancement with perspective correction for clearer multipage PDF outputs.
CamScanner’s core scan workflow combines camera capture with image enhancement, perspective correction, and contrast tuning so documents remain readable after scanning. Output controls include multipage PDF creation and export options for sending files as documents rather than raw images. User-facing organization features cover page ordering and trimming so scanned sets can match a consistent layout.
Integration and automation depth are limited for enterprise governance because the product experience centers on device capture and file sharing, not admin provisioning. A common tradeoff appears in automation and API surface area since external systems need more than a file drop to enforce schemas and RBAC workflows. CamScanner fits teams that route scanned documents for review and forwarding, while higher-control organizations typically require tighter audit log and policy enforcement patterns.
- +Image enhancement and perspective correction improve readability
- +Multipage PDF export supports consistent document sets
- +Page reordering and cropping help fix capture issues
- +Cross-device syncing supports ongoing document handoff
- –Enterprise admin provisioning controls are not the primary focus
- –Automation and API surface area for workflows looks limited
- –Audit log and RBAC configuration are not apparent in standard use
Field sales teams
Capture contracts during site visits
Fewer rescan requests
Accounts payable staff
Digitize supplier invoices for approval
Faster invoice routing
Show 2 more scenarios
Facilities and inspections teams
Record maintenance checklists
Improved documentation consistency
Creates organized document sets that can be shared for compliance review.
Legal operations teams
Scan exhibit packets for review
Quicker document preparation
Packages exhibits into multipage PDFs with cropping and page order controls.
Best for: Fits when teams need quick scanned PDFs for review and sharing without heavy admin automation.
More related reading
Adobe Acrobat
PDF OCRPDF toolkit that supports scanning from compatible devices, OCR text recognition, and automated export and indexing within enterprise workflows.
Built-in OCR for scanned pages that embeds searchable text into the resulting PDF.
Adobe Acrobat fits scanning teams that must turn paper or images into searchable PDFs and then refine layout and content before sharing. OCR can create text layers in PDFs so downstream tools can search and copy content. Editing at the page and object level supports cleanup of scanned artifacts like skew, noise, and incorrect orientation.
A tradeoff appears when organizations need deep scanner-to-schema automation, since Acrobat’s scanning controls are more document-focused than data-first. For high-throughput ingest, Acrobat’s workflow quality depends on how documents are batch prepared and routed after scanning. Best fit shows up when review, redaction, and formatting matter before export into case systems or archives.
- +OCR creates searchable text inside PDFs for scanned pages
- +Page-level editing supports cleanup before sharing or archiving
- +Redaction and PDF security controls reduce exposure risk
- +Annotation and comment workflows speed review cycles
- –Automation is more document workflow oriented than schema driven
- –Batch ingest throughput depends on external scripting and routing
- –Integration depth requires combining Acrobat with other systems
Legal operations teams
Convert filings into searchable, redacted PDFs
Faster review and discovery
AP document teams
Standardize scanned invoices for posting
Lower rework volume
Show 2 more scenarios
Records management teams
Archive scanned records with access controls
More consistent archival
PDF security and consistent PDF output support retention and controlled distribution.
Customer support teams
Turn screenshots into searchable case attachments
Quicker case resolution
OCR and annotation help transform image-based inputs into searchable, commentable evidence.
Best for: Fits when teams need searchable PDFs plus review and redaction before system handoff.
Google Drive
document captureMobile scanning capture that converts documents to PDFs and uses OCR so extracted text is searchable within Drive.
Shared Drives with RBAC-based membership and role inheritance across files and folders.
Google Drive supports a clear data model with Drive files, revisions, and parents via the Drive API, plus shared Drives that structure collaboration across departments. The integration surface includes the Drive API for file operations, permissions, and change tracking, plus the Drive Activity feed that supports external workflows. Through extensibility in Google Workspace, Drive content can be connected to external systems for indexing, capture, and downstream processing pipelines.
A core tradeoff is that Drive is not a dedicated scanning workflow engine, so scan capture, OCR configuration, and queue orchestration require external systems or Google-native tooling. Google Drive fits when documents originate outside the system, then need governed storage, RBAC-aligned access, and auditability for ongoing collaboration and retrieval.
- +Drive API supports file CRUD, revisions, and permission operations
- +Shared Drives organize content with membership-based access control
- +Change feeds enable automation for downstream document processing
- –No built-in capture queue for scan ingestion and retry logic
- –Fine-grained document classification often needs external metadata strategy
Records management teams
Centralize scanned files with governance
Consistent access and audit trace
Integration engineers
Automate ingestion and processing triggers
Higher throughput for back-office workflows
Show 1 more scenario
IT admins
Control external sharing and access
Reduced compliance and access risk
Apply Workspace admin configuration and audit log settings to govern sharing scope and access events.
Best for: Fits when governed storage and Drive API automation matter more than built-in scan capture orchestration.
Tesseract
open-source OCROpen-source OCR engine that runs locally and can be embedded into scanning pipelines with configurable language packs and layout options.
CLI-driven OCR with selectable traineddata and adjustable recognition parameters like page segmentation mode.
Tesseract is an OCR engine that converts images to text using a configurable data model of language training and recognition parameters. The distinct value comes from tight integration into existing scanning pipelines through CLI invocation and process-level APIs exposed via wrappers.
Core capabilities include multi-language models, layout-aware recognition options, and tunable character and segmentation settings that affect accuracy and throughput. Integration depth is driven by how outputs are represented as plain text or structured variants in downstream code, with automation handled outside the engine via scripts and service wrappers.
- +Runs locally with CLI control for predictable scanning pipeline behavior
- +Language model selection via traineddata files enables targeted OCR accuracy
- +Tunable recognition and segmentation parameters support accuracy and throughput tradeoffs
- +Extensible integration through wrappers, custom scripts, and batch processing
- –No built-in API governance features like RBAC or audit logs
- –No first-party admin console for provisioning, configuration, or policy management
- –Structured outputs require downstream parsing rather than native schema exports
- –Operational automation depends on external orchestration and wrapper code
Best for: Fits when teams need OCR integration in existing scanning workflows with parameter tuning and local execution.
Google Cloud Vision API
API OCRAPI that performs OCR and document text detection on images with configurable language hints for programmatic extraction from scans.
Document OCR with layout-focused results for form-like pages and multi-block text extraction.
Google Cloud Vision API ingests images and returns OCR text, label detection, and structured metadata through a single HTTP API. It also supports document OCR, handwriting recognition, face detection, logo detection, and landmark detection for common scanning workflows.
The API exposes versioned request parameters that control feature selection and output formats, which makes integration and testing predictable. Operations integrate with Google Cloud IAM, audit logging, and service-level configuration for access control and governance.
- +Strong OCR controls with document OCR modes and layout-aware outputs
- +Wide vision feature set covers labels, landmarks, faces, and logos in one API
- +Predictable REST API schema with feature-specific request parameters
- +Works with Google Cloud IAM and audit logs for governance and traceability
- –Higher complexity when combining multiple detectors in one pipeline
- –Output schemas vary by feature, requiring per-feature parsing logic
- –Throughput and batching need careful design to avoid latency bottlenecks
- –Region and resource configuration can complicate cross-project deployments
Best for: Fits when teams need an API-first visual scanning pipeline with OCR, document analysis, and IAM governance.
AWS Textract
API document OCRManaged OCR and document analysis service that extracts text and structured data from scanned documents via APIs.
Asynchronous DocumentAnalysis jobs with structured results for forms and tables that can be polled and retrieved programmatically
AWS Textract fits teams that need OCR and structured extraction with direct integration into AWS workflows and governance tooling. It converts scanned documents and images into text plus structured data using a document analysis data model that maps detected forms, tables, and key-value pairs.
Its asynchronous APIs support large batch workloads with status polling and results retrieval for higher throughput. Integration depth is reinforced through event-driven patterns with AWS services and a clear automation surface via the Textract API.
- +Document analysis extracts forms, tables, and key-value fields from images
- +Asynchronous jobs support high-volume extraction with status polling
- +Strong AWS integration fits S3, event triggers, and downstream processing pipelines
- +Workflow automation is driven through a well-scoped Textract API
- –Data model is tied to AWS patterns for storage, job management, and retrieval
- –Configuration choices affect schema stability across document layouts
- –Table reconstruction quality can vary for skewed, low-contrast, or dense pages
- –Human review tooling requires building additional UI and reconciliation logic
Best for: Fits when document ingestion pipelines in AWS must extract text, forms, and tables at scale via API automation.
Azure AI Vision
API OCRVision API features text extraction for scanned images with programmatic document OCR and downstream integration into analytics pipelines.
Document OCR with structured extraction for fields and layout, returned via an automation-friendly REST response.
Azure AI Vision combines image analysis endpoints with a managed data model for OCR, tagging, and visual features. It fits scanning workflows that require deterministic automation through documented REST APIs and SDKs for enrichment at ingestion time.
Identity and policy controls tie into Azure RBAC and monitoring surfaces that support audit log review. The automation surface spans text extraction and structured output generation that downstream systems can schema-validate.
- +REST API for OCR and visual analysis outputs structured, schema-friendly JSON
- +Azure RBAC and resource-level controls support governed access for vision workloads
- +SDKs and pipeline-ready automation reduce glue code for batch and event workflows
- +Telemetry and logs integrate with Azure monitoring for operational verification
- +Extensibility through custom configuration and model routing across services
- –Throughput tuning and batching strategy require careful client-side orchestration
- –Model-specific confidence and field coverage vary by document quality and layout
- –Schema changes in downstream parsing often need versioned mapping code
- –Governance requires consistent tagging and monitoring conventions across environments
Best for: Fits when teams need governed, API-first vision scanning with OCR and structured outputs tied to Azure controls.
Rossum
document extractionDocument processing platform that ingests scans, extracts fields using configurable data models, and exposes workflow integrations for automation.
Schema-driven extraction with configurable confidence-based review through the API and workflow configuration.
Rossum is document AI scanning software that focuses on invoice and back-office extraction with an explicit data model and schema-driven results. It uses configurable capture workflows plus human-in-the-loop review to correct fields that confidence scoring flags.
Integration centers on API-first extraction, task management, and extensibility hooks that connect scans to downstream systems. Governance is supported through role-based access controls and audit visibility for review and changes.
- +Schema-first data model for consistent field extraction across document types
- +API surface for starting scans, fetching results, and handling asynchronous jobs
- +Human-in-the-loop review supports field-level corrections and revalidation
- +RBAC and change audit logging help control who can edit extracted data
- +Workflow configuration enables repeatable processing without custom code
- –Document-type setup can require schema and labeling work before automation stabilizes
- –High throughput depends on job queue behavior and integration polling design
- –Automation logic is workflow-focused and less suited for arbitrary rule engines
- –OCR and extraction quality may still need ongoing training for edge layouts
Best for: Fits when mid-volume invoice processing needs schema control, API automation, and governed human review.
OpenCV
scan preprocessingComputer vision library used to build scan preprocessing such as deskew, binarization, and perspective correction before OCR.
Matrix-based image representation with consistent C++ and Python APIs for high-throughput processing pipelines.
OpenCV provides image and video processing primitives for building scanning pipelines from frames or camera streams. It has extensive computer vision modules, including feature detection, geometric transforms, and OCR-adjacent preprocessing hooks.
Integration depth comes from a C++ core with Python bindings, plus interoperable data structures that can feed custom detection, segmentation, and measurement workflows. Automation relies on external orchestration since OpenCV offers an API and configurable algorithms rather than a workflow engine with a governed data model.
- +C++ core with Python bindings for mixed automation and scripting workloads
- +Rich vision modules for preprocessing, detection, and geometric correction stages
- +Extensible APIs for custom operators using the same memory and matrix model
- +Deterministic, offline library behavior suited for batch scanning throughput
- –No built-in scanning workflow UI or job scheduling capabilities
- –No native RBAC, audit log, or admin governance controls
- –Data model and schema design must be implemented outside OpenCV
- –OCR, document parsing, and labeling require additional libraries and glue code
Best for: Fits when teams need programmable scanning logic with a library API and controlled deployment around OpenCV.
Imgix
image normalizationImage transformation service that can normalize scan images for downstream OCR by applying resizing and format transforms through APIs.
Request-time URL transformations that combine resize, crop, format, and quality in one API call.
Imgix serves production image transformation via a documented URL API, making integration depth a core strength. Its data model centers on an image “source” plus transformation parameters like crop, resize, format, and quality.
Automation and API surface focus on request-time configuration that can be standardized through reusable patterns in applications. Admin governance is comparatively light, with control largely exercised through access to endpoints and upstream configuration rather than workflow-level RBAC.
- +URL API supports resize, crop, format, and quality transformations per request
- +Request-time configuration reduces need for preprocessing pipelines
- +Origin integration aligns transformations with existing storage and CDN setups
- +Consistent transformation parameters enable schema-like reuse in application code
- –Workflow governance and RBAC are limited compared with scanning-style admin tools
- –Automation relies on URL construction, not event-driven transformation orchestration
- –Audit logging and administrative reporting are not emphasized for governance needs
- –Fine-grained throughput controls require careful CDN and cache configuration
Best for: Fits when teams need high-throughput, on-demand image transformations with controlled request parameters and minimal pipeline overhead.
How to Choose the Right Scanning Solutions Software
This buyer's guide covers scanning solutions software used for document capture, OCR, and extraction across tools like CamScanner, Adobe Acrobat, Google Drive, Tesseract, Google Cloud Vision API, AWS Textract, Azure AI Vision, Rossum, OpenCV, and Imgix.
The guide maps integration depth, data model design, automation and API surface, and admin and governance controls to concrete capabilities in those tools, so selection decisions can be made on integration breadth and control depth instead of ad hoc workflows.
Scanning pipelines that turn images into governed text and structured fields
Scanning solutions software captures documents as images or PDFs, runs OCR, and routes outputs into storage, review, or downstream systems using an explicit integration surface. It solves problems like producing searchable PDFs, extracting structured fields like forms and tables, and keeping access controls and audit trails aligned with business workflows.
Tools like Adobe Acrobat focus on OCR embedded into PDFs plus page-level cleanup before handoff. API-first platforms like AWS Textract and Google Cloud Vision API focus on OCR and structured results returned through REST APIs for automation.
Evaluation criteria for integration, schema control, and governed automation
Integration depth determines whether a tool can fit into existing systems through file APIs, image transformation APIs, or OCR service APIs rather than relying on manual exports. Data model clarity determines how reliably a tool maps detected text and fields into schema-like outputs that downstream systems can validate and store.
Automation and API surface decide whether batch processing can be orchestrated through job calls, status polling, or event-driven patterns. Admin and governance controls determine whether RBAC, audit logging, and policy configuration can be operated as a system rather than a set of user habits.
RBAC and audit visibility for extracted documents
Google Drive provides Shared Drives with RBAC-based membership and role inheritance across files and folders, which fits governed storage workflows. Rossum includes RBAC and audit visibility for review and changes, which supports controlled corrections of extracted fields.
Schema-driven extraction for forms, tables, and key-value fields
AWS Textract uses a document analysis data model that maps detected forms, tables, and key-value pairs into structured results. Rossum uses a schema-first data model for consistent field extraction across document types with configurable capture workflows.
API-first automation with predictable OCR request and response structures
Google Cloud Vision API exposes a versioned HTTP API for document OCR and layout-focused results, which supports programmatic parsing and testing. Azure AI Vision returns structured extraction via an automation-friendly REST response and ties access controls to Azure policy and monitoring surfaces.
Asynchronous processing for high-volume throughput
AWS Textract provides asynchronous DocumentAnalysis jobs with status polling and results retrieval, which supports large batch workloads without tying up interactive request threads. Rossum also runs extraction workflows through its API surface and relies on polling and task behavior for throughput.
Capture and cleanup controls that produce usable PDFs and multipage outputs
Adobe Acrobat embeds OCR searchable text inside PDFs and supports page-level editing like cropping, rotation, and redaction for review and secure sharing. CamScanner produces multipage PDF outputs with on-device enhancement and perspective correction that improves readability without requiring separate preprocessing.
Integration through file APIs versus integration through image transformation parameters
Google Drive enables automation through the Drive API with file CRUD, revisions, changes feeds, and permission operations. Imgix supports request-time URL transformations with resize, crop, format, and quality parameters, which standardizes preprocessing before OCR in external pipelines.
A decision framework for selecting the right OCR and scanning integration path
Start by mapping the required output type to the tool’s actual data model and output behavior. Searchable PDFs point to Adobe Acrobat or CamScanner, while structured extraction for forms and tables points to AWS Textract or Rossum.
Next map governance and automation to the tool’s real integration surface. Google Drive ties OCR and storage into governed Drive identity and APIs, while Google Cloud Vision API and Azure AI Vision provide REST endpoints designed for schema-like response handling and IAM governance.
Define the output contract: searchable PDF versus structured fields
Choose Adobe Acrobat when the deliverable must be a PDF with embedded OCR text plus page-level cleanup and redaction controls. Choose AWS Textract or Rossum when the deliverable must include structured extraction for forms, tables, or key-value fields that a system can store and validate.
Test integration depth against existing systems and identity
Use Google Drive when governed storage, metadata, and permissions operations must stay inside Drive through the Drive API and Shared Drives. Use Google Cloud Vision API or Azure AI Vision when OCR must plug into a service architecture through REST APIs tied to IAM or Azure RBAC and monitoring.
Plan automation around the tool’s job and polling model
If batch volume is high, prioritize AWS Textract because DocumentAnalysis runs as asynchronous jobs with status polling and programmatic result retrieval. If extraction work needs human review loops on confidence flags, prioritize Rossum because its API-driven workflow includes human-in-the-loop corrections and revalidation.
Evaluate governance controls for edits, access, and traceability
Choose tools with explicit RBAC and audit visibility in the scanning workflow, like Rossum for review and changes. Choose Google Drive when RBAC and role inheritance across files and folders must govern who can access scanned documents and derived OCR content.
Select preprocessing scope based on where image normalization happens
If preprocessing must be built and tuned inside a pipeline, use OpenCV for deskew, binarization, and perspective correction with Python bindings and a C++ core. If preprocessing needs on-demand normalization tied to storage and CDN setups, use Imgix for request-time URL transformations that standardize resize, crop, format, and quality.
Which teams should buy which scanning integration approach
Scanning solutions software choices depend on whether the primary need is readable PDFs, API-driven OCR, or schema-like extraction that supports automation and governed review. The tools below match specific patterns of capture, automation, and governance.
The selection hinges on integration breadth and control depth across file APIs, REST APIs, job orchestration, and RBAC plus audit visibility.
Operations teams producing searchable PDFs for review and archiving
Adobe Acrobat fits workflows that require OCR embedded into PDFs plus page-level editing and redaction controls before system handoff. CamScanner fits teams that need quick multipage PDF outputs with on-device perspective correction for improved readability.
Engineering teams building API-first OCR pipelines with governance
Google Cloud Vision API fits API-first visual scanning with document OCR and layout-focused extraction returned through a predictable REST interface plus IAM governance integration. Azure AI Vision fits governed, API-first vision scanning where structured JSON-like responses align with schema validation and Azure RBAC controls.
Back-office processing teams extracting invoice or document fields with schema control
Rossum fits mid-volume invoice and back-office extraction needs because it uses a schema-first data model plus configurable capture workflows. AWS Textract fits organizations that need forms, tables, and key-value extraction at scale using asynchronous DocumentAnalysis jobs.
Platform teams integrating scanning results into governed storage and permissions models
Google Drive fits when scanned documents must live inside Drive and share permissions through Shared Drives with role inheritance. Its Drive API automation supports file CRUD, revisions, changes feeds, and permissions management that reduce glue code.
AI and computer vision teams building custom preprocessing and OCR orchestration
OpenCV fits programmable scanning logic because it provides geometric correction and binarization primitives with a consistent C++ and Python API model. Tesseract fits local OCR integration where traineddata selection and page segmentation parameters can be tuned via CLI-driven pipelines.
Common procurement pitfalls that break scanning automation and governance
The most frequent failures come from mismatching tool output type to downstream expectations or underestimating the operational work required to govern extraction and edits. Several tools have limitations around admin provisioning, RBAC, audit logging, or structured schema exports.
These pitfalls can be avoided by validating the tool’s actual API behavior, data model structure, and workflow governance controls during implementation planning.
Buying a document workflow tool when structured extraction schema is required
Adobe Acrobat excels at OCR inside PDFs and page-level redaction, but it is more document workflow oriented than schema driven for automated field extraction. AWS Textract and Rossum provide structured results for forms, tables, and key-value fields that support automation.
Assuming local OCR engines include governance controls
Tesseract focuses on local OCR with CLI control and traineddata language selection, and it does not provide native RBAC or audit logging. Teams that need governance should pair OCR output with systems that implement RBAC and audit logs, or use API services like Google Cloud Vision API or Azure AI Vision that integrate with IAM and policy controls.
Under-specifying asynchronous job orchestration for high-volume ingestion
Google Cloud Vision API and Azure AI Vision support REST calls, but high throughput still requires client-side batching and orchestration to avoid latency bottlenecks. AWS Textract reduces orchestration burden by using asynchronous DocumentAnalysis jobs with status polling and result retrieval.
Treating image preprocessing as a separate project without a standardized input contract
OpenCV provides deskew, binarization, and perspective correction, but it has no built-in workflow UI or job scheduling, so external orchestration and schema design are required. Imgix can standardize request-time crop and resize parameters that help keep preprocessing consistent when OCR runs downstream.
How We Selected and Ranked These Tools
We evaluated CamScanner, Adobe Acrobat, Google Drive, Tesseract, Google Cloud Vision API, AWS Textract, Azure AI Vision, Rossum, OpenCV, and Imgix using features, ease of use, and value, with features carrying the most weight at forty percent. Ease of use and value each account for thirty percent, and the overall rating is a weighted average of those three factors. This scoring reflects editorial research tied to the concrete capabilities and limitations described for each tool, such as OCR output type, job orchestration patterns, and governance controls, not hands-on lab testing or private benchmarks.
CamScanner stands apart by delivering on-device scan enhancement with perspective correction that produces clearer multipage PDF outputs, which lifted it through the features factor more than governance-heavy tools whose standout value centers on structured extraction or async job APIs.
Frequently Asked Questions About Scanning Solutions Software
Which tools are best for API-first OCR and document analysis?
When should an organization use Google Drive versus a dedicated scanning pipeline?
How do SSO and access controls typically differ across these tools?
What is the tradeoff between CamScanner-style capture apps and Acrobat-style document workflows?
Which toolchain supports human-in-the-loop corrections for structured fields?
How does data model design affect integrations for invoices and forms?
What are the main operational differences between synchronous OCR calls and asynchronous batch jobs?
How can admin controls and audit visibility be handled when scans are processed programmatically?
What extensibility options exist beyond OCR for custom document pipelines?
Conclusion
After evaluating 10 data science analytics, CamScanner 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.comimgix.comReferenced in the comparison table and product reviews above.
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