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Business FinanceTop 10 Best Automated OCR Software of 2026
Discover top automated OCR software for accurate text extraction.
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.
Google Cloud Vision OCR
Text detection with document-level OCR capabilities via Vision API
Built for teams building automated OCR pipelines with Google Cloud integrations.
Amazon Textract
Forms and Tables feature that outputs structured key-value pairs and table cells
Built for teams automating OCR for forms and tables with structured outputs.
Microsoft Azure AI Document Intelligence
Custom model training for document-specific extraction with confidence per field
Built for teams automating structured extraction from mixed document types at scale.
Related reading
Comparison Table
This comparison table benchmarks automated OCR software used to extract text from scanned documents, images, and PDFs. It contrasts major options including Google Cloud Vision OCR, Amazon Textract, Microsoft Azure AI Document Intelligence, ABBYY FlexiCapture, and ABBYY Cloud OCR SDK across extraction quality, document-processing capabilities, and integration patterns.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | Google Cloud Vision OCR Extracts text from images and documents using OCR with multilingual support and form text features in a managed API. | API-first | 8.5/10 | 9.0/10 | 7.8/10 | 8.7/10 |
| 2 | Amazon Textract Performs OCR and structured extraction from scanned documents with tables and forms support via a managed AWS service. | enterprise API | 8.1/10 | 8.6/10 | 7.9/10 | 7.7/10 |
| 3 | Microsoft Azure AI Document Intelligence Uses OCR and document layout analysis to extract text, key-value pairs, and tables from images and PDFs via Azure APIs. | document AI | 8.2/10 | 8.7/10 | 7.8/10 | 7.9/10 |
| 4 | ABBYY FlexiCapture Automates high-accuracy document capture workflows with OCR and validation designed for enterprise document processing. | enterprise automation | 8.1/10 | 8.8/10 | 7.6/10 | 7.7/10 |
| 5 | ABBYY Cloud OCR SDK Provides cloud OCR for extracting text from images with developer-oriented APIs for integration into business processes. | developer API | 7.9/10 | 8.2/10 | 7.4/10 | 7.9/10 |
| 6 | Rossum Automates document OCR and extraction for business workflows with configurable models for invoices, forms, and finance documents. | workflow automation | 8.2/10 | 8.6/10 | 7.8/10 | 8.0/10 |
| 7 | Kofax Capture Automates document scanning and OCR capture with classification, validation, and downstream workflow integration. | capture platform | 8.0/10 | 8.6/10 | 7.4/10 | 7.7/10 |
| 8 | UiPath Document Understanding Extracts structured data from document images with OCR and layout understanding capabilities for automated back-office processes. | RPA document AI | 7.8/10 | 8.2/10 | 7.4/10 | 7.6/10 |
| 9 | Hyperscience Automates OCR and document data extraction for finance and operations using classification, extraction, and human-in-the-loop review. | AI document processing | 7.6/10 | 8.2/10 | 7.0/10 | 7.3/10 |
| 10 | Parascript Automates OCR and handwriting-aware text extraction for business documents using enterprise APIs and document understanding workflows. | enterprise OCR | 7.3/10 | 8.0/10 | 6.8/10 | 7.0/10 |
Extracts text from images and documents using OCR with multilingual support and form text features in a managed API.
Performs OCR and structured extraction from scanned documents with tables and forms support via a managed AWS service.
Uses OCR and document layout analysis to extract text, key-value pairs, and tables from images and PDFs via Azure APIs.
Automates high-accuracy document capture workflows with OCR and validation designed for enterprise document processing.
Provides cloud OCR for extracting text from images with developer-oriented APIs for integration into business processes.
Automates document OCR and extraction for business workflows with configurable models for invoices, forms, and finance documents.
Automates document scanning and OCR capture with classification, validation, and downstream workflow integration.
Extracts structured data from document images with OCR and layout understanding capabilities for automated back-office processes.
Automates OCR and document data extraction for finance and operations using classification, extraction, and human-in-the-loop review.
Automates OCR and handwriting-aware text extraction for business documents using enterprise APIs and document understanding workflows.
Google Cloud Vision OCR
API-firstExtracts text from images and documents using OCR with multilingual support and form text features in a managed API.
Text detection with document-level OCR capabilities via Vision API
Google Cloud Vision OCR stands out with production-grade OCR delivered through Google Cloud APIs for images and PDFs. It supports text detection, handwriting recognition, and document-oriented extraction using configurable feature sets. Integrations with other Google Cloud services enable automation patterns like labeling, entity extraction, and downstream processing of extracted text. Strong developer controls and model options make it a fit for automated OCR pipelines needing repeatable results at scale.
Pros
- High-accuracy text detection with strong document and form extraction
- API support for handwriting recognition and multiple OCR feature types
- Automation-ready integration with Google Cloud services and event-driven workflows
- Batch processing options and robust request controls for pipeline consistency
Cons
- Primarily API-driven, making setup heavier than point-and-click OCR
- Output quality can vary for heavily skewed, low-resolution, or noisy scans
- Tuning OCR workflows requires engineering effort and iterative testing
Best For
Teams building automated OCR pipelines with Google Cloud integrations
More related reading
Amazon Textract
enterprise APIPerforms OCR and structured extraction from scanned documents with tables and forms support via a managed AWS service.
Forms and Tables feature that outputs structured key-value pairs and table cells
Amazon Textract stands out for turning documents into structured text using OCR plus form and table extraction. It can detect printed and handwritten text and return results with bounding boxes and confidence scores for downstream automation. It also supports analyzing scanned documents and multi-page PDFs through managed APIs, enabling ingestion into document workflows without custom computer-vision pipelines.
Pros
- Extracts key-value pairs and tables from forms using a single API
- Returns word-level bounding boxes and confidence for validation workflows
- Handles scanned PDFs and images with automatic page processing
- Supports handwriting alongside printed text for mixed-content documents
Cons
- Workflow quality depends heavily on document layout consistency
- Configuring table and form outputs often requires iterative tuning
- Model performance varies across low-resolution or skewed scans
- Complex fields need post-processing to normalize extracted data
Best For
Teams automating OCR for forms and tables with structured outputs
Microsoft Azure AI Document Intelligence
document AIUses OCR and document layout analysis to extract text, key-value pairs, and tables from images and PDFs via Azure APIs.
Custom model training for document-specific extraction with confidence per field
Microsoft Azure AI Document Intelligence stands out for its managed, model-driven document understanding over raw scans and PDFs. It extracts structured data and text using prebuilt layouts like forms, receipts, and invoices plus custom models for document-specific schemas. Confidence scoring and field-level outputs support downstream automation without hand-built parsing logic. It also integrates directly with Azure for event-driven workflows and searchable storage of extracted content.
Pros
- Prebuilt layouts cover forms, invoices, receipts, and ID documents
- Field-level structured outputs reduce post-processing for automation
- Custom model training supports domain-specific document formats
Cons
- Higher accuracy for custom needs labeled data and tuning
- Complex documents with heavy tables can require additional normalization
- Workflow assembly still needs engineering around storage and labeling
Best For
Teams automating structured extraction from mixed document types at scale
ABBYY FlexiCapture
enterprise automationAutomates high-accuracy document capture workflows with OCR and validation designed for enterprise document processing.
Confidence-based review routing with validation rules for extracted fields
ABBYY FlexiCapture focuses on automated document capture with OCR plus rule-based and ML-assisted classification to extract fields from forms, invoices, and structured documents. It supports training and configuration to recognize layouts, validate data, and route documents through capture workflows. The solution also integrates with external systems for handoff of extracted results, including confidence-driven review queues for low-confidence reads.
Pros
- Strong automated extraction for invoices and forms with layout-aware capture
- Field validation and confidence scoring reduce manual rekeying
- Configurable workflows with review queues for uncertain documents
- Broad output integration options for downstream processing
Cons
- Setup and training require technical configuration for best results
- Performance depends heavily on document quality and layout consistency
- Complex projects can need ongoing model and rules maintenance
Best For
Operations teams automating invoice and form extraction with controlled document layouts
ABBYY Cloud OCR SDK
developer APIProvides cloud OCR for extracting text from images with developer-oriented APIs for integration into business processes.
Structured OCR results with bounding regions and confidence for reliable post-processing
ABBYY Cloud OCR SDK focuses on production-grade text recognition via cloud APIs, with support for multiple document layouts and languages. It captures structured output such as detected text, bounding regions, and confidence signals that help automated pipelines verify OCR quality. The SDK design supports batch processing and integration into backend services for document ingestion workflows.
Pros
- Cloud API output includes text with bounding data for automation
- Multi-language OCR and layout handling suit varied document sources
- Confidence and structured results support QA and downstream rules
Cons
- Less suited for fully offline OCR because recognition runs in the cloud
- Integration effort rises when tuning for noisy scans and forms
- Advanced automation depends on building workflow and validation logic
Best For
Teams building automated document ingestion pipelines with cloud OCR
Rossum
workflow automationAutomates document OCR and extraction for business workflows with configurable models for invoices, forms, and finance documents.
Human-in-the-loop correction that retrains extraction models for document-specific accuracy
Rossum stands out by turning document capture into configurable document processing workflows driven by machine learning. It extracts fields from varied document layouts and routes results into downstream systems using integrations and API access. The platform focuses on automating invoice, contract, and back-office document data entry with human-in-the-loop correction to improve accuracy over time.
Pros
- Field extraction from messy layouts with continuous learning from corrections
- Human-in-the-loop review improves model accuracy on real document sets
- Workflow automation for invoices and back-office documents with integrations
- API access supports custom pipelines and downstream system updates
Cons
- Setup requires careful document classification and labeling for best results
- Complex workflows need more configuration effort than simple OCR tools
- Less suitable for one-off scans without ongoing training loops
Best For
Teams automating invoice and back-office document data extraction with review workflows
More related reading
Kofax Capture
capture platformAutomates document scanning and OCR capture with classification, validation, and downstream workflow integration.
Capture workflow configuration with validation-driven field extraction and indexing
Kofax Capture centers on automated document ingestion that routes captured documents into business processes with OCR-backed extraction. It supports capture workflows with configurable index fields, barcodes, and document separation so extracted text lands in usable metadata. Advanced document processing capabilities include classification and validation rules that reduce manual corrections for structured forms and scanned files.
Pros
- Configurable capture workflows combine OCR with indexing and validation rules.
- Strong handling of batch scanning with document separation and page-level processing.
- Good support for form-like documents via field mapping to downstream systems.
- Integration options for enterprise content and workflow destinations.
Cons
- Setup and workflow configuration can require specialized capture process knowledge.
- Best results depend on consistent document quality and layout stability.
- Complex deployments take more effort than simpler single-task OCR tools.
Best For
Enterprise teams automating form ingestion into workflow systems with rules
UiPath Document Understanding
RPA document AIExtracts structured data from document images with OCR and layout understanding capabilities for automated back-office processes.
Human-in-the-loop document training for improving OCR extraction accuracy over time
UiPath Document Understanding centers on document AI extraction built for feeding structured fields into automation workflows. It supports template-free processing for varied document layouts and pairs OCR with classification and field extraction to reduce manual setup. The extracted data can be routed into UiPath processes for downstream checks, validation, and system updates. The main limitation is that accuracy and layout robustness depend on the quality and coverage of training and document variety.
Pros
- Template-free extraction for structured fields across varied document layouts
- Strong integration with UiPath automation for end-to-end processing workflows
- Human-in-the-loop tools for review and correction to improve extraction accuracy
- Document classification helps route documents to the right extraction logic
Cons
- Model quality depends heavily on training data coverage and consistency
- Complex setups can require expertise beyond basic OCR needs
- Handling extreme layout drift may need iterative retraining and tuning
Best For
Teams automating document-heavy workflows with extraction to drive business processes
Hyperscience
AI document processingAutomates OCR and document data extraction for finance and operations using classification, extraction, and human-in-the-loop review.
Hyperscience Hyperscience AI-powered document extraction with learnable models for field accuracy
Hyperscience stands out with machine-learning document understanding that extracts structured fields from varied document types and routes them into downstream systems. It supports automated intake workflows, including IDP-style OCR plus classification and data extraction for invoices, forms, and back-office documents. Accuracy is improved through labeling and model training, and outputs can be delivered via integrations for processing records at scale.
Pros
- ML-driven document understanding goes beyond OCR into classification and field extraction
- Workflow automation handles multi-document processing with structured outputs
- Model training improves extraction accuracy for specific document collections
- Integrations support pushing extracted data into business systems
Cons
- Setup and training effort can be heavy for small document volumes
- Best results depend on consistent document quality and labeling
- Generic OCR use cases may feel overpowered versus simpler tools
Best For
Operations teams automating invoice and form processing with structured data extraction
Parascript
enterprise OCRAutomates OCR and handwriting-aware text extraction for business documents using enterprise APIs and document understanding workflows.
Document extraction with confidence-scoring and validation rules
Parascript stands out with document AI for automating forms and unstructured document capture using recognition that targets business documents. It supports OCR plus document classification, field extraction, and rules-based validation to turn scanned pages into structured data. It also offers deployment options for embedding OCR into capture pipelines and for integrating with enterprise systems that need consistent extraction. The focus is on reliable extraction workflows rather than general-purpose image enhancement.
Pros
- Document understanding supports field extraction beyond plain OCR
- Built-in validation rules improve data quality for captured forms
- Flexible integration patterns fit enterprise capture workflows
Cons
- Setup and tuning for document layouts can be time-consuming
- Less suited for ad hoc OCR with highly diverse inputs
- Workflow automation benefits require stronger process design
Best For
Teams automating form-heavy document capture into validated structured data
Conclusion
After evaluating 10 business finance, Google Cloud Vision OCR 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.
How to Choose the Right Automated OCR Software
This buyer's guide explains how to select Automated OCR software for production document capture and extraction. It covers Google Cloud Vision OCR, Amazon Textract, Microsoft Azure AI Document Intelligence, ABBYY FlexiCapture, ABBYY Cloud OCR SDK, Rossum, Kofax Capture, UiPath Document Understanding, Hyperscience, and Parascript. The guide maps concrete capabilities like forms and tables extraction, confidence scoring, and human-in-the-loop training to real document workflows.
What Is Automated OCR Software?
Automated OCR software converts scanned documents and image files into machine-readable text using OCR plus layout understanding for structured extraction. It also reduces manual rekeying by extracting fields like key-value pairs, tables, and form data with confidence signals and validation logic. Teams use these systems to automate invoice processing, back-office document intake, and document routing into downstream workflow systems. Tools like Amazon Textract and Microsoft Azure AI Document Intelligence exemplify end-to-end extraction for forms and tables or custom document schemas.
Key Features to Look For
The right feature set determines whether extracted text becomes validated structured data or raw OCR output that still needs heavy cleanup.
Document-level OCR and multi-feature extraction
Look for OCR that supports document-level processing and multiple extraction feature types for repeatable pipeline results. Google Cloud Vision OCR provides text detection with document-level OCR capabilities and handwriting recognition, and it is built for configurable OCR feature sets in a managed API.
Forms and tables output with structured cells and key-value pairs
Choose tools that output structured form fields and table cells, not only plain text lines. Amazon Textract returns forms and tables in a single API with structured key-value pairs and table cells, and it provides word-level bounding boxes and confidence for validation workflows.
Confidence scoring and bounding regions for QA and validation
Require confidence signals and spatial outputs so automation can detect low-quality reads and trigger downstream rules. ABBYY Cloud OCR SDK outputs text with bounding regions and confidence for reliable post-processing, and ABBYY FlexiCapture uses confidence scoring with validation-driven review queues for uncertain fields.
Custom model training and schema-specific extraction
Select solutions that support custom model training when documents vary by business domain or require specific field definitions. Microsoft Azure AI Document Intelligence offers custom model training with confidence per field, and Rossum retrains extraction models through human-in-the-loop corrections to improve accuracy on real document sets.
Human-in-the-loop review and retraining workflows
Automated OCR succeeds when uncertain extractions enter a review loop and improve over time. Rossum centers its workflow on human-in-the-loop correction that retrains models for document-specific accuracy, and UiPath Document Understanding provides human-in-the-loop document training to improve extraction accuracy over time.
Capture workflow automation with validation, indexing, and routing
If the goal includes moving documents through intake into business systems, prioritize capture workflows that combine OCR with classification, validation rules, and indexing. Kofax Capture supports capture workflow configuration with validation-driven field extraction and indexing, and Hyperscience automates intake workflows using classification plus structured extraction routed into downstream integrations.
How to Choose the Right Automated OCR Software
Selection should start from the target document types and the level of structure required in the output, then match that to extraction, validation, and workflow automation capabilities.
Match the output structure to the document type
If the requirement is extracting forms and table data as usable structured fields, Amazon Textract is built around forms and tables with structured key-value pairs and table cells. If mixed document types require prebuilt layouts like invoices and receipts plus custom schemas, Microsoft Azure AI Document Intelligence provides prebuilt layouts and custom model training for field-level structured output.
Confirm whether handwriting and noisy inputs are part of the workflow
If documents include handwritten notes or mixed printed and handwritten fields, Amazon Textract and Google Cloud Vision OCR both support handwriting recognition and printed plus handwriting detection. If outputs need higher QA rigor under imperfect scans, tools that emit confidence and bounding data like ABBYY Cloud OCR SDK and ABBYY FlexiCapture support validation-driven workflows that catch low-confidence reads.
Plan for validation and confidence-driven review queues
When automation must reduce rekeying without silently propagating errors, choose confidence scoring plus validation rules that route uncertain extractions for review. ABBYY FlexiCapture uses confidence-based review routing with validation rules, and Parascript includes confidence-scoring and validation rules for document extraction into validated structured data.
Decide how much model training and tuning effort is acceptable
If document layouts require domain-specific extraction performance, Microsoft Azure AI Document Intelligence and Rossum support custom modeling and retraining through corrections. If the workflow is more engineering-driven with configurable OCR feature sets, Google Cloud Vision OCR fits teams building automated pipelines that need repeatable request controls and model options.
Ensure the intake workflow can route into downstream systems
If the goal is document ingestion into business processes with indexing and routing, Kofax Capture provides capture workflow configuration with validation-driven field extraction and indexing. For end-to-end back-office document automation with integrations and retraining, Rossum and Hyperscience both route extracted fields into downstream systems via API access and integrations.
Who Needs Automated OCR Software?
Automated OCR targets teams that need scalable extraction from scanned documents and want structured, validated outputs instead of plain text.
Teams building automated OCR pipelines with Google Cloud integrations
Google Cloud Vision OCR fits organizations that want an API-first workflow with document-level OCR and handwriting recognition delivered through Google Cloud services. This approach reduces friction for event-driven automation patterns built on managed requests and configurable feature sets.
Teams automating forms and tables into structured data
Amazon Textract is purpose-built for forms and tables output with structured key-value pairs and table cells. It also returns bounding boxes and confidence so workflow logic can validate extracted fields for downstream automation.
Teams automating structured extraction from mixed document types at scale
Microsoft Azure AI Document Intelligence is designed for mixed document types using prebuilt layouts like forms, receipts, and invoices plus custom models for domain schemas. Its field-level outputs and confidence per field reduce the need for manual parsing logic.
Operations teams automating invoice and back-office document extraction with review workflows
Rossum is built for invoice, contract, and back-office document data extraction with human-in-the-loop correction and continuous learning. Hyperscience supports learnable models with labeling and routing of structured extraction into downstream systems for multi-document intake.
Common Mistakes to Avoid
Common failure points across Automated OCR tools come from mismatching document structure to the extraction mode, underplanning for validation, and underestimating setup and tuning requirements.
Choosing OCR without structured output for forms and tables
Teams that only accept plain text output often end up rebuilding parsing logic for fields and table cells. Amazon Textract and Microsoft Azure AI Document Intelligence provide structured extraction for forms and tables with field-level outputs so automation can consume the results directly.
Skipping confidence and validation routing for low-quality reads
Automation without confidence-based validation can propagate errors from skewed, low-resolution, or noisy scans into downstream systems. ABBYY FlexiCapture and Parascript both emphasize confidence scoring with review routing or validation rules to keep uncertain fields out of automatic data entry.
Assuming layout variability can be handled without training or tuning
Systems that require engineering effort often perform poorly when document layouts drift far from training or expectations. Microsoft Azure AI Document Intelligence custom model training, Rossum retraining through corrections, and UiPath Document Understanding human-in-the-loop training address layout variability by improving models on document-specific patterns.
Overbuilding a capture workflow when an API-first OCR approach is sufficient
Full capture workflow orchestration can be unnecessary when the only requirement is text recognition plus bounding and confidence for downstream rules. Google Cloud Vision OCR and ABBYY Cloud OCR SDK fit engineering-led ingestion pipelines where OCR outputs feed validation logic instead of full document capture indexing.
How We Selected and Ranked These Tools
we evaluated every tool on three sub-dimensions: features with weight 0.4, ease of use with weight 0.3, and value with weight 0.3. The overall rating is the weighted average of those three sub-dimensions using overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. Google Cloud Vision OCR separated on features because it combines document-level OCR and handwriting recognition with configurable feature sets and strong request controls for automation pipelines, which supports structured document extraction at scale rather than only basic OCR.
Frequently Asked Questions About Automated OCR Software
Which automated OCR tool is best for structured extraction from forms and tables?
Amazon Textract is designed to output key-value pairs and table cells with bounding boxes and confidence scores, which makes it effective for form and table automation. Microsoft Azure AI Document Intelligence also produces field-level outputs with confidence scoring using prebuilt models for common document types like receipts and invoices.
Which tool fits an automated OCR pipeline that must use document-level OCR on images and PDFs?
Google Cloud Vision OCR supports text detection and document-oriented extraction through Vision API feature sets for images and PDFs. ABBYY Cloud OCR SDK supports batch ingestion and structured OCR results with bounding regions and confidence signals for automated post-processing.
How do tools differ in handling handwriting and mixed printed plus handwritten documents?
Amazon Textract detects printed and handwritten text and returns results with confidence scoring for downstream automation. Google Cloud Vision OCR also supports handwriting recognition, which helps pipelines that must extract from stamps, notes, and signed documents.
What automated OCR options reduce custom parsing logic for common business documents?
Microsoft Azure AI Document Intelligence reduces custom parsing by using managed, model-driven layouts for forms, receipts, and invoices plus confidence-based field outputs. Rossum also routes extracted fields into workflows with human-in-the-loop correction that improves extraction accuracy for document-specific layouts over time.
Which platform is strongest for document capture workflows that include validation and confidence-driven review?
ABBYY FlexiCapture supports rule-based and ML-assisted extraction plus validation rules and confidence-driven review queues for low-confidence fields. Kofax Capture similarly focuses on configurable capture workflows with index fields, document separation, and validation-driven field extraction that reduces manual rework.
Which tool is best when document layouts vary heavily and extraction must be template-free?
UiPath Document Understanding supports template-free processing for varied layouts by combining OCR with classification and field extraction, then routing results into UiPath processes. Hyperscience also performs ML-based document understanding for invoices and forms by extracting fields and delivering results through integrations at scale.
Which automated OCR tools provide bounding regions and confidence signals for quality checks?
ABBYY Cloud OCR SDK outputs detected text along with bounding regions and confidence signals to help automated systems validate OCR quality. Google Cloud Vision OCR supports configurable feature sets for repeatable extraction, while Amazon Textract attaches confidence scoring to both text and structured fields like table cells.
How do these tools integrate into enterprise workflows after OCR extraction?
Rossum uses API access and integrations to route extracted fields into downstream systems and relies on human-in-the-loop correction to retrain models. Kofax Capture focuses on feeding extracted text into business processes by using configurable index fields and workflow handoff for structured ingestion.
What are common failure modes in automated OCR, and how do tools mitigate them?
Low confidence reads and layout drift often cause wrong field mapping, and ABBYY FlexiCapture mitigates this with validation rules and confidence-driven review routing. UiPath Document Understanding mitigates layout variability by using human-in-the-loop training, while Parascript adds rules-based validation on top of OCR and classification for more reliable structured data outputs.
Tools reviewed
aws.amazon.comReferenced in the comparison table and product reviews above.
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