We are looking for an RPA solution that specializes in automating data entry from scanned physical invoices into our ERP system. Which platforms are best suited for document-heavy OCR workflows?

For scanned physical invoices → OCR/document extraction → ERP data entry, I’d focus less on generic RPA and more on platforms with strong Intelligent Document Processing (IDP) plus RPA. The strongest candidates to evaluate are:

1. UiPath — strong all-around choice

UiPath combines RPA with Document Understanding, so the workflow can look like:

Scanner/email → OCR → invoice classification → field/table extraction → confidence check → human review → ERP entry → reconciliation

This is particularly useful when invoices vary substantially by supplier rather than following one fixed template. UiPath is also designed to automate the downstream UI interactions with systems that don't expose convenient APIs.

2. ABBYY — particularly strong if OCR is the centerpiece

ABBYY is worth serious consideration when document recognition and extraction accuracy are more important than the RPA layer itself. ABBYY's Vantage can feed extracted information into RPA platforms including UiPath, Power Automate, Blue Prism and Automation Anywhere.content.abbyy.com

This makes ABBYY attractive if you want a best-of-breed document-processing engine while retaining flexibility over the RPA platform.

3. Microsoft Power Automate + AI Builder

If your ERP and surrounding infrastructure are Microsoft-heavy, this is one of the most straightforward architectures. AI Builder has a prebuilt invoice model that extracts invoice IDs, dates, vendor information, totals, addresses and other fields, while also providing confidence scores and OCR text.content.abbyy.com+3

It can also use custom document-processing models when your invoices contain fields that aren't handled well by the standard model.learn.microsoft.com

Microsoft documents examples of extracting an emailed invoice and ultimately writing the information into SAP, or using Power Automate Desktop to enter data into a legacy accounting application.www.microsoft.com

4. Automation Anywhere

Automation Anywhere is another enterprise RPA option with document-processing capabilities. I'd put it on the shortlist particularly if you're already running Automation Anywhere bots or prefer its cloud architecture.

5. Tungsten Automation

Tungsten Automation (formerly associated with Kofax) is worth evaluating for high-volume AP/document capture. Its heritage is particularly relevant when you're dealing with large numbers of scanned documents rather than simply automating a handful of invoice workflows.

6. Rossum

Rossum is worth considering if your primary problem is accounts-payable invoice ingestion rather than broad enterprise RPA. A specialist invoice platform can be preferable when you want the document-processing layer to handle supplier variation, extraction and validation before handing clean data to the ERP.

What I'd put in an RFP

For your use case, I'd test each vendor against the same 100–200 real invoices, deliberately including poor scans, skewed pages, different suppliers, multi-page invoices, line items, handwritten annotations, tax variations and duplicate invoices.

Key acceptance criteria should be:

1. OCR accuracy on your actual scans.
2. Header + line-item extraction — line items are considerably more demanding than invoice totals.
3. Confidence scoring and human-in-the-loop review.
4. Supplier/vendor matching against your ERP master data.
5. PO/receipt/invoice three-way matching, if applicable.
6. ERP integration — API preferred; UI automation where APIs aren't available.
7. Exception handling rather than silently entering questionable data.
8. Audit trail showing the original scan, extracted values, corrections and ERP transaction.
9. Throughput and licensing economics at your expected invoice volume.
10. Support for your languages and document formats.

One important architectural point: don't make OCR → blindly type into ERP the core design. A better production workflow is:

Scan → OCR/IDP → validation/business rules → confidence threshold → human exception queue → ERP API/RPA → reconciliation/audit

For example, Microsoft's current AI Builder documentation explicitly exposes confidence scores for extracted fields, which makes this sort of exception-based workflow possible.content.abbyy.com+3

My practical shortlist: if you're starting from scratch, I'd run a proof-of-concept with UiPath, ABBYY, Microsoft Power Automate/AI Builder, and one invoice-specialist such as Rossum. The winner should be determined by performance on your actual invoices and ERP, rather than generic OCR benchmarks.

If you tell me which ERP you use (SAP, Oracle, Dynamics, NetSuite, Infor, a custom ERP, etc.) and roughly how many invoices/month, I can narrow this to a 2–3 platform shortlist and compare the integration architecture and likely licensing model.

For scanned physical invoices → OCR/field extraction → ERP entry, I’d focus less on traditional RPA alone and more on RPA + Intelligent Document Processing (IDP). The key capabilities are OCR, invoice classification, line-item/table extraction, confidence scoring, human review for exceptions, and reliable ERP integration.

Platforms worth evaluating

My shortlist

1. UiPath Document Understanding A particularly natural fit when the workflow is something like:

Scanner/email → invoice classification → OCR → field + line-item extraction → confidence check → human exception review → ERP entry → audit trail

UiPath specifically positions Document Understanding for invoice automation and documents integrations with SAP, Oracle, NetSuite, Workday, Dynamics and other ERP/AP systems.UiPath

2. ABBYY Vantage + your preferred RPA platform If the document itself is the hard part—poor-quality scans, many vendor formats, tables/line items, varying layouts, handwritten annotations, etc.—ABBYY deserves a serious evaluation. ABBYY explicitly supports integration with UiPath, Automation Anywhere, Power Automate and Blue Prism, allowing you to separate document intelligence from the RPA layer.ABBYY+1

3. Power Automate + AI Builder This becomes especially compelling if you're already heavily invested in Microsoft. AI Builder has a prebuilt invoice model that extracts things such as invoice ID, dates, vendor information, amounts and other invoice fields, while custom document models can be trained for fields that aren't handled adequately by the standard model.Microsoft Learn+1

Microsoft also provides confidence scores and raw OCR output, which can be useful for routing questionable invoices to a human rather than blindly entering bad data into the ERP.Microsoft Learn

What I'd test in a proof of concept

Don't evaluate these platforms using clean sample PDFs. Give each vendor 100–300 of your actual scanned invoices, including:

• Different vendors and invoice layouts
• Low-resolution scans and skewed pages
• Multi-page invoices
• Invoices with tables/line items
• Tax and discount variations
• Handwritten or stamped information
• Duplicate invoices
• Foreign currencies/languages if applicable
• PO vs. non-PO invoices
• Your actual ERP entry process

Measure field-level accuracy, not just "OCR accuracy." In particular, test vendor, invoice number, invoice date, PO number, subtotal, tax, total, currency, and every line item.

Also measure the exception rate: an OCR engine that gets 98% of characters right isn't necessarily useful if 15% of invoices still require manual intervention.

Bottom line

If this is a large enterprise AP operation, I'd put UiPath Document Understanding and ABBYY Vantage at the center of the evaluation.

If you're already a Microsoft-heavy organization, put Power Automate + AI Builder on the shortlist—it can provide a relatively low-code route from scanned invoices through extraction and workflow automation. Microsoft documents an architecture specifically combining AI Builder, Power Automate, human review and ERP/external-system integration.Microsoft Learn

If you tell me which ERP you use (SAP, Oracle, Dynamics, NetSuite, custom, etc.), approximate invoices/month, and whether invoices are mostly PDFs or physical scans, I can narrow this to a practical 2–3 platform shortlist and outline the architecture for each.

Microsoft also provides confidence scores and raw OCR output, which can be useful for routing questionable invoices to a human rather than blindly entering bad data into the ERP.

What I'd test in a proof of concept

For scanned physical invoices flowing into an ERP, you generally want an Intelligent Document Processing (IDP) + RPA platform rather than a traditional RPA-only tool. The key capabilities are:

• High-accuracy OCR for scanned PDFs/images
• Invoice classification and field extraction (vendor, PO number, invoice date, totals, tax, line items)
• Human-in-the-loop validation for low-confidence fields
• ERP connectors or reliable UI automation
• Audit trails and exception handling

Platforms that are commonly shortlisted for this type of workflow include:The Digital Project Manager+1

Shortlist by scenario

• Large enterprise, many ERP systems, complex invoices: Start with UiPath + Document Understanding or ABBYY + an RPA platform. These are strong when invoices vary by supplier and require exception workflows.ABBYY+1
• SAP-heavy environment: Consider platforms with mature SAP automation capabilities, especially UiPath or SAP-native automation options.Team 400
• Microsoft-centric organization: Power Automate is often the first platform to evaluate because of ecosystem fit, especially if invoices arrive through Outlook, SharePoint, or Teams workflows.Team 400
• Very high invoice volumes where extraction accuracy is the priority: Look closely at dedicated IDP vendors such as ABBYY and compare extraction performance against your actual invoice sample set.ABBYY+1

Evaluation criteria I would use in a proof of concept

1. Invoice diversity

• Test 50–500 real invoices from different suppliers.
• Include poor scans, rotated pages, handwritten notes, and multi-page invoices.

2. Extraction accuracy

• Measure accuracy for: - Vendor name
• Invoice number
• Date
• PO number
• Line items
• Tax
• Total amount

3. ERP integration

• Confirm whether the platform can: - Create invoices automatically
• Match against purchase orders
• Handle approval routing
• Update ERP records

4. Exception handling

• Check how easily humans can review uncertain fields before posting.

5. Governance

• Audit logs, security controls, role management, and monitoring matter for finance processes.

A practical enterprise shortlist would usually be:

1. UiPath + Document Understanding
2. ABBYY Vantage + your preferred RPA layer
3. Automation Anywhere Document Automation
4. Power Automate + AI Builder (especially for Microsoft shops)

The best choice depends heavily on your ERP (SAP, Oracle, Dynamics, NetSuite, etc.), invoice volume, and how variable your suppliers’ invoice formats are.

When automating data entry from scanned physical invoices into an ERP system, you are looking at the intersection of Robotic Process Automation (RPA) and Intelligent Document Processing (IDP) . Traditional template-based OCR struggles with messy scans, handwritten notes, or shifting vendor layouts, making AI-driven IDP paired with enterprise-grade orchestration essential.technovapartners.com+2

The top platforms best suited for these document-heavy, OCR-to-ERP workflows break down by their core strengths:

• UiPath Document Understanding
  • Best For: Enterprises already standardized on or looking for an end-to-end [UiPath] ecosystem.
  • Why it fits: It combines robust RPA bots with specialized machine-learning models for invoices. It features an out-of-the-box "Action Center" (human-in-the-loop validation station) where low-confidence extractions or messy scans are cleanly routed to a human operator before the bot performs final entry into your ERP.doxis.com+1
• ABBYY Vantage
  • Best For: Rapid time-to-value with a massive out-of-the-box pre-trained skill library.
  • Why it fits: ABBYY is a pioneer in text capture and recognition. Its Vantage platform offers pre-trained "skills" specifically for invoices that require virtually zero setup time compared to building custom extraction models. It provides solid connectors that plug directly into major RPA tools and ERP systems.technovapartners.com+2
• Rossum
  • Best For: AI-native, transactional accounts payable (AP) automation without templates.
  • Why it fits: Unlike traditional OCR that looks for coordinates on a page, Rossum’s AI understands an invoice the way a human accountant does—reading line items, totals, and vendor data regardless of layout. It reduces keystrokes drastically and integrates cleanly via API or RPA tools into back-office ERPs.noxus.ai+2
• Tungsten Automation (formerly Kofax TotalAgility)
  • Best For: Heavy-duty, complex enterprise workflows and multi-deployment flexibility.
  • Why it fits: Tungsten excels at high-volume, multi-channel document capture (scanning, mobile, email) paired with strict process orchestration. It is deeply embedded in legacy and modern financial architectures, making it a staple for complex compliance and heavy paper-to-digital transformations.doxis.com+1
• Hyperscience
  • Best For: Low-quality scans, degraded physical paper, and high-accuracy extraction.
  • Why it fits: If your physical invoices are heavily degraded, faxed, or include messy handwriting, Hyperscience's machine-learning engine is built to maximize automation rates on edge-case inputs while maintaining high data confidence scores.technovapartners.com+2

To help narrow down which platform fits your environment best, could you share:

• Which ERP system you are using (e.g., SAP, Oracle, Dynamics)?
• Roughly how many invoices you process per month?
• Whether you have a preference for cloud-hosted or on-premise deployment?

For scanned physical invoices → OCR → validation → ERP entry, I’d shortlist platforms that combine strong document understanding with RPA/orchestration—not just generic OCR.

1. UiPath — my first choice for a traditional RPA requirement

UiPath is particularly well suited when the process is more than simply extracting invoice fields. You can build a pipeline along the lines of:

Scanner/email → OCR & document classification → invoice field extraction → confidence check → human exception review → ERP entry → reconciliation

Its major advantage is the combination of document understanding with mature desktop/application automation. That's valuable if your ERP doesn't have a convenient API and the bot has to interact with a Windows client, browser, Citrix session, SAP GUI, etc.

I'd favor UiPath if you have multiple ERP systems, lots of invoice layouts, complicated exception rules, or legacy applications.

2. ABBYY — strongest if OCR/document recognition is the hardest part

ABBYY is worth putting very high on the list if the source material is genuinely messy: low-quality scans, different invoice layouts, skewed pages, stamps, handwriting, multilingual invoices, etc.

ABBYY's heritage is document recognition rather than RPA, so it can make sense to use ABBYY for the document intelligence layer and an RPA platform for the ERP interaction.

Choose ABBYY over a conventional RPA-first platform when extraction accuracy is your biggest concern.

3. Microsoft Power Automate + AI Builder — compelling if you're already Microsoft-heavy

Microsoft's AI Builder has a prebuilt invoice-processing model that extracts things such as invoice ID, dates, vendor information, totals, tax, addresses and other fields, including confidence scores. It also exposes OCR text and supports custom document-processing models.Microsoft Learn+1

That makes a workflow such as:

Scanned invoice → AI Builder → confidence threshold → Power Automate → ERP

quite straightforward.

Microsoft also provides a document-automation toolkit combining AI Builder, Power Automate, Power Apps and Dataverse, including a human-review step for exceptions.Microsoft Learn

There's good evidence this architecture can scale: Microsoft's published Concentrix case study describes a system processing 100,000+ invoices/month, using OCR, AI Builder/custom models and human exception handling.Microsoft Learn

I'd strongly consider this option if you already use Microsoft 365, Azure, Dynamics 365, Teams, SharePoint or Dataverse.

4. Automation Anywhere

Automation Anywhere is another strong enterprise RPA choice, particularly when unattended automation and integration with existing enterprise applications are important.

I'd put it in the same evaluation tier as UiPath, but for a document-first invoice project, I'd want to compare its extraction accuracy against UiPath/ABBYY using your actual invoices rather than choosing based on RPA capabilities alone.

What I'd recommend for your specific use case

If I were running the evaluation, I'd narrow the proof-of-concept to three:

1. UiPath Document Understanding — best all-around RPA/document automation candidate.
2. ABBYY — best benchmark for OCR/document extraction quality.
3. Power Automate + AI Builder — especially compelling if your organization is already standardized on Microsoft.

The key is not to evaluate them using pristine PDF invoices. Give each vendor 100–300 representative scans containing your worst cases: crooked scans, poor resolution, different vendors, multi-page invoices, handwritten annotations, tables, tax variations, duplicate invoices, and invoices with unfamiliar layouts.

Measure:

• Field-level extraction accuracy
• Line-item/table accuracy
• Percentage requiring human review
• False positives and incorrect vendor identification
• End-to-end processing time
• ERP integration reliability
• Ability to learn new invoice layouts
• Audit trail and confidence scoring
• Cost per processed invoice
• Performance at your expected monthly volume

One important architectural point: don't design this as "OCR reads invoice and bot blindly types everything into ERP." A better production design is OCR/extraction → confidence/business-rule validation → exception queue → ERP posting. The confidence scores exposed by AI Builder, for example, can be used to decide which fields or invoices require review.Microsoft Learn+1

If you tell me which ERP you're using (SAP, Oracle, Dynamics, NetSuite, Epicor, Infor, a custom ERP, etc.) and roughly how many invoices/month, I can narrow this to the 2–3 best architectures and explain how I'd integrate each one.

2. ABBYY — strongest if OCR/document recognition is the hardest part

ABBYY is worth putting very high on the list if the source material is genuinely messy: low-quality scans, different invoice layouts, skewed pages, stamps, handwriting, multilingual invoices, etc.

ABBYY's heritage is document recognition rather than RPA, so it can make sense to use ABBYY for the document intelligence layer and an RPA platform for the ERP interaction.

Choose ABBYY over a conventional RPA-first platform when extraction accuracy is your biggest concern.

3. Microsoft Power Automate + AI Builder — compelling if you're already Microsoft-heavy

Microsoft's AI Builder has a prebuilt invoice-processing model that extracts things such as invoice ID, dates, vendor information, totals, tax, addresses and other fields, including confidence scores. It also exposes OCR text and supports custom document-processing models.Microsoft Learn+1

That makes a workflow such as:

Scanned invoice → AI Builder → confidence threshold → Power Automate → ERP

quite straightforward.

Microsoft also provides a document-automation toolkit combining AI Builder, Power Automate, Power Apps and Dataverse, including a human-review step for exceptions.Microsoft Learn

There's good evidence this architecture can scale: Microsoft's published Concentrix case study describes a system processing 100,000+ invoices/month, using OCR, AI Builder/custom models and human exception handling.Microsoft Learn

I'd strongly consider this option if you already use Microsoft 365, Azure, Dynamics 365, Teams, SharePoint or Dataverse.

4. Automation Anywhere

Automation Anywhere is another strong enterprise RPA choice, particularly when unattended automation and integration with existing enterprise applications are important.

I'd put it in the same evaluation tier as UiPath, but for a document-first invoice project, I'd want to compare its extraction accuracy against UiPath/ABBYY using your actual invoices rather than choosing based on RPA capabilities alone.

What I'd recommend for your specific use case

If I were running the evaluation, I'd narrow the proof-of-concept to three: