Document Processing For Accounts Payable | Accounts Payable OCR
Table of Contents
Introduction
In any business, no matter the size or industry, Accounts Payable (AP) is the beating heart of financial operations. It’s the system that ensures vendors are paid on time, expenses are tracked properly, and cash flow remains steady. Yet, for all its importance, AP is often bogged down with paperwork—invoices, purchase orders, credit memos, remittance slips, and whatnot—each coming in different formats, layouts, and languages.
Over the past few years, the volume of vendor documents has exploded. Global supply chains, remote operations, and increasing vendor diversification mean finance teams are now dealing with thousands of documents each month. Imagine manually entering every line item from an invoice or cross-checking a handwritten remittance note—it’s not just tedious, it’s risky and error-prone.
That’s where OCR for accounts payable comes into play.
OCR (Optical Character Recognition) has been around for a while. But today, with the help of smarter document workflows, OCR accounts payable solutions have evolved to do much more than just “read” text. They can understand context, maintain layout fidelity, and accurately extract structured information, making them a vital tool in modern AP processing.
In this article, we’ll explore:
What accounts payable document processing really involves
Why accounts payable OCR software is becoming the default
Common challenges in automation and how the best OCR tools overcome them
And a step-by-step guide to using platforms like Unstract to extract AP data from scanned, photographed, or digital documents
Let’s begin by understanding the actual workflow behind AP document processing.
What is Accounts Payable Document Processing?
At its core, accounts payable document processing is the system of managing, reviewing, and processing various financial documents tied to the money a business owes to its vendors or suppliers. But the reality is more complex.
The Typical AP Workflow
Here’s a simplified flow that most finance teams follow:
Receive the documents: Invoices, remittance slips, vendor onboarding forms, and purchase orders—usually via email, fax, post, or scanned copies.
Extract the information: Details like invoice number, date, line items, amounts, vendor details, and payment terms.
Validate against PO: Cross-check whether the invoice matches the purchase order or contract.
Route for approval: Share with department heads or finance approvers.
Enter into ERP: Finally, input all relevant data into the company’s accounting system or ERP software.
Schedule payment: Once verified, schedule and track the payment.
Now imagine doing this for hundreds of documents every week. That’s where accounts payable OCR solutions begin to show their real value.
Common Document Types in AP
Invoices: Standard, pro forma, and credit-based, often differing across vendors and countries.
Credit Memos: Issued when there’s a return or billing error.
Remittance Advice: Sent by customers or partners confirming payment details.
Purchase Orders (POs): Outlines the agreed terms before an invoice is raised.
Each of these documents can come in vastly different layouts and often include a mix of typed text, handwritten notes, tables, and even stamps. Relying on manual entry is not just inefficient—it’s a compliance risk.
Traditional vs. Modern AP Document Processing
The Old Way: Traditionally, AP teams relied on manual entry or basic OCR tools that weren’t designed specifically for finance. They often had to adjust settings for each new format, perform post-processing for layout issues, or manually correct recognition errors. Not only was this time-consuming, but it also introduced opportunities for error, especially when dealing with international or scanned documents.
The Modern Way: Today, modern accounts payable OCR software goes beyond simple text recognition. It can:
Recognize and understand complex document structures (e.g., multi-page invoices with tables)
Maintain accuracy across formats without reconfiguration
Integrate with finance systems or ERPs
Reduce processing time from hours to minutes
The best OCR software for accounts payable doesn’t require you to code or build templates. Instead, it works out of the box and learns from corrections to get smarter over time.
Real-World Need for OCR in AP
Consider this: You receive a scanned invoice from a vendor in Spain, a faxed remittance slip from a local supplier, and a photographed air waybill from a logistics partner. Manually processing these would take hours. With a modern OCR accounts payable system, these can be uploaded, interpreted, and sent to your finance system in under 5 minutes.
The result? Fewer delays, faster approvals, and zero backlogs.
Why Accurate OCR is Crucial in Accounts Payable
In the world of accounts payable (AP), even the smallest data entry error can result in major downstream problems—from missed payments and late fees to compliance violations and strained vendor relationships. That’s why the accuracy of your OCR (Optical Character Recognition) solution plays a critical role in ensuring the smooth functioning of AP operations.
The Role of OCR in AP
OCR technology is used to digitize printed, scanned, or photographed documents by converting the text into machine-readable data. In accounts payable workflows, OCR tools are used to capture important financial details such as:
Invoice numbers
Dates of issue and due
Line item descriptions
Tax and total amounts
Vendor details and addresses
Without OCR, teams are left to manually type in this data, which is both time-consuming and prone to human error.
But here’s the catch: not all OCR is equal. Basic OCR engines often struggle with:
Low-quality scans
Handwritten notes
Complex tables
Multi-column formats
Variations in invoice templates across vendors
That’s why businesses are now looking beyond traditional OCR and investing in accounts payable OCR software designed specifically for finance-related documents.
Real Impact of OCR Errors
Poor OCR quality doesn’t just cause a few typos. It leads to real-world business issues, such as:
Error Type
Business Impact
Misread Invoice Number
Duplicate payments or processing failures
Incorrect Date Recognition
Missed payment deadlines, late fees
Skipped Line Items
Incomplete financial records
Misplaced Vendor Info
Errors in financial reporting or fraud exposure
Misidentified Totals
Budgeting discrepancies or incorrect approvals
Why Layout Preservation Matters
One of the most essential features of modern OCR for accounts payable is its ability to preserve the original layout of documents. This matters because:
Tables with line items must be captured exactly as they appear
Labels like “Subtotal,” “Tax,” and “Total Due” must be linked to their values
Multi-page invoices must be processed holistically, not as fragmented parts
Best OCR software for accounts payable ensures layout fidelity, meaning data is extracted with full context, not just raw text.
In short, accuracy isn’t a luxury in AP—it’s a necessity. The more precise your OCR, the fewer issues you’ll face downstream. That’s why it’s crucial to use tools built specifically for OCR accounts payable workflows.
Why Accounts Payable Needs Automation
Accounts payable is one of the most repetitive and time-sensitive functions in any organization. Whether it’s processing invoices, matching purchase orders, or approving payments, every step involves a cycle of document review and validation. As business operations grow, manual AP processes simply can’t keep up.
The Growing Need for Automation
There are multiple pain points that AP teams face:
High volume of documents at month-end and year-end
Long approval cycles and bottlenecks
Risk of human error and duplicate payments
Lack of visibility into payment timelines
Increased pressure to reduce operational costs
Let’s compare traditional AP vs. automated AP to see the impact clearly:
Feature
Traditional Accounts Payable
Automated with OCR Software
Data Entry
Manual, slow, and error-prone
Fast, automated, and consistent
Invoice Matching
Manual comparison with PO
Rule-based automated matching
Approval Routing
Email or paper-based
Digital, with notifications and tracking
Exception Handling
Reactive
Proactive with validation rules
Reporting
Delayed and limited
Real-time dashboards
Cost
High FTE cost
Reduced overhead and processing cost
OCR and Automation: A Perfect Match
By incorporating OCR into the AP automation pipeline, companies can:
Digitally scan and interpret documents at scale
Integrate data directly into ERP systems
Enable real-time validation and approvals
Track and audit every step for compliance
Especially during end-of-month or fiscal year-end, when invoice volumes spike, OCR for accounts payable ensures no document is missed, and no vendor is left unpaid.
Benefits for AP Teams and the Business
Faster turnaround: Process hundreds of invoices in minutes.
Improved accuracy: Minimize human errors.
Cost savings: Reduce dependency on large AP teams.
Vendor satisfaction: Pay vendors on time and maintain trust.
Audit readiness: Digital trails and error-free documentation.
Choosing the Right OCR Software for AP
When selecting the best OCR software for accounts payable, look for features like:
Document type support (invoices, credit notes, vendor forms)
OCR accuracy for scanned and photographed documents
Integration with your existing ERP or accounting software
Layout preservation for tables and line items
Support for handwritten notes and multi-language documents
The right software doesn’t just automate—it transforms the way finance teams operate, bringing reliability, speed, and intelligence into a traditionally manual domain.
Common Challenges in Accounts Payable Document Automation
As more businesses adopt accounts payable OCR software to streamline their operations, they often encounter significant hurdles that can limit the effectiveness of automation. The real world of financial documentation is far from standardized—and this diversity can strain even the most advanced automation systems.
Document Format Diversity
In AP workflows, companies deal with dozens or even hundreds of vendors, each using different document templates. Some invoices are structured neatly in columns, while others may resemble free-form letters. Even within the same vendor, formats may change over time or vary based on the department or region.
This lack of standardization makes it difficult for traditional OCR systems to extract data reliably. For example:
Vendor Type
Invoice Variation Example
Local suppliers
Handwritten invoices with basic totals
Global vendors
PDFs with embedded tables and multiple currencies
Freelancers
One-page word docs with scattered information
This diversity leads to frequent errors in ocr accounts payable workflows unless the OCR solution can adapt intelligently.
Scanned, Faxed, or Handwritten Documents
Not all incoming documents are digitally clean. Many businesses still:
Fax their invoices
Scan paper receipts
Send photos of handwritten notes
Email PDF scans with skewed or faded layouts
OCR engines without robust layout correction or handwriting recognition can fail to detect critical details like invoice amounts, tax rates, or payment terms.
This is where traditional OCR for accounts payable solutions often struggles—resulting in data loss, missed payments, or the need for manual correction.
Poor Image Quality and Irregular Layouts
Accounts payable documents come in all conditions:
Crumpled scans
Tilted or off-center pages
Overlapping stamps or signatures
Colored backgrounds or watermark noise
These visual irregularities create real barriers for basic OCR tools. Without advanced pre-processing and semantic understanding, even the best-designed OCR logic can break.
Hence, companies are turning to smarter solutions that combine ocr accounts payable software with AI pre-processing tools to overcome these issues.
Why is it difficult to parse text from PDFs?
Not all PDFs contain actual text—many are just scanned or photographed images of pages. In such cases, the only way to extract text is by converting the PDF pages into images and running them through an OCR tool. The OCR output then needs to be reconstructed into readable text.
However, scanned PDFs often come with quality issues. Image artifacts, poor lighting, skewed angles, or compression can significantly reduce OCR accuracy—especially if the PDF contains a photo of a document rather than a clean scan.
Even if OCR data is extracted accurately, another challenge arises—how do you push this data into your existing systems?
Most enterprises still rely on legacy ERPs or custom-built finance software, which often lack:
APIs for smooth integration
Modern UIs to handle structured JSON inputs
Data validation checks that align with automated outputs
This leads to delays and manual workarounds, which defeats the purpose of automation. The best OCR software for accounts payable includes connectors or middleware to integrate seamlessly with systems like SAP, Oracle Financials, QuickBooks, or NetSuite.
The Rise of AI & LLMs in Document Processing
With the limitations of traditional OCR becoming more evident, businesses are looking towards AI-powered approaches—specifically Large Language Models (LLMs)—to redefine accounts payable document automation.
Why Traditional OCR Falls Short
OCR alone only converts images to text. It lacks semantic understanding, which is crucial when dealing with documents where:
The same word means different things based on context
Key information is not explicitly labeled
Multiple similar fields (e.g., billing vs shipping address) exist in the same document
For example, a traditional OCR engine may extract the line: “Due 30 days after invoice date.”
But it won’t infer the exact payment due date, nor link it to invoice terms for automation.
This is a big gap for accounts payable OCR software, especially when documents come from varied sources.
Enter AI: Interpreting Unstructured Data
AI systems, particularly those trained on large financial datasets, bring contextual awareness to the table. They can:
Identify patterns across different vendor formats
Interpret labels even if they’re phrased differently (e.g., “Amount Payable” vs. “Total Due”)
Flag anomalies in invoice values or mismatches in purchase orders
This means AP teams can move beyond just reading documents to understanding them, allowing for more intelligent routing, validation, and approvals.
LLMs: Making AP Automation Truly Smart
Large Language Models like GPT-4 are capable of semantic extraction, which is game-changing for document-heavy processes like AP.
Here’s what LLMs add to the AP automation stack:
Capability
Benefit for AP Documents
Context-Aware Field Extraction
Knows when a “total” is final or intermediate
Semantic Matching
Links invoice with PO by understanding content, not format
Natural Language Understanding
Extracts unlabelled fields using context clues
Summarization
Condenses invoice or credit note terms into structured data
For example, from a vendor note that says: “A 10% discount will be applied if paid within 15 days.”
The future of accounts payable OCR lies in hybrid models where:
OCR handles the raw text extraction
LLMs interpret the meaning and structure the data
This synergy allows systems to accurately process invoices, remittance forms, vendor onboarding documents, and more—even if they’re scanned, complex, or messy.
Platforms like Unstract and tools like LLMWhisperer are already paving the way for this next generation of intelligent document processing, designed specifically for AP teams.
Applying AI to Accounts Payable Document Types
When it comes to automating the accounts payable process, one size definitely does not fit all. Each document type—whether it’s an invoice, a vendor registration form, a remittance advice, or an air waybill—has its own structure, semantics, and extraction challenges. Here’s how AI and advanced accounts payable OCR solutions make sense of them.
Invoices
Invoices are the most common document processed in AP automation workflows. They often include a mix of structured header data, semi-structured line items, and unstructured notes. Traditional OCR tools can extract text, but struggle with understanding relationships between fields (e.g., matching a line item with tax).
AI-powered accounts payable OCR software enhances this process by:
Identifying fields such as Invoice Number, PO Number, Invoice Date, and Due Date
Parsing line items: Item Description, Unit Price, Quantity, Total
Capturing tax-related fields: VAT, GST, Withholding Tax, etc.
By leveraging OCR for accounts payable combined with semantic understanding via LLMs, this process is now seamless, scalable, and accurate.
Vendor Forms
Vendor onboarding is critical for accounts payable compliance. These documents vary greatly—some are Excel sheets, others are scanned PDFs, and some come in hand-filled templates.
Intelligent OCR software for accounts payable, powered by AI, can extract:
Vendor Name
Contact Person
Tax Identification Number (TIN)
Bank Account Details
Business Address & Communication Info
This structured data can be directly synced with ERP systems like SAP or Oracle, reducing onboarding delays and minimizing manual data entry errors.
Credit Memos & Remittance Advice
Credit memos and remittance advice forms are essential for payment reconciliation. They often reference multiple invoices, partial payments, or discounts.
AI plays a key role here by:
Parsing document references for cross-validation
Extracting Invoice Numbers, Credit Amounts, Reasons for Credit
Identifying payment terms and reconciliation codes
These documents can be hard to parse due to their free-text nature, but accounts payable OCR software with LLM capabilities handles them well.
Air Waybills (AWB)
Air Waybills are standard in logistics-related accounts payable. These forms may include data like:
Carrier Information
Booking Numbers
Itemized Cargo List
Weight and Dimensions
Freight Charges and Delivery Address
OCR systems often fail when faced with AWBs captured via phone cameras or poorly scanned. However, tools like Unstract’s LLMWhisperer, paired with LLM extraction, deliver 100% layout fidelity and accurate structured data, regardless of document condition.
By applying AI to these diverse AP document types, businesses reduce processing time, increase data accuracy, and enhance vendor relationships—all while lowering the manual effort involved.
Meet Unstract: AI-Powered IDP for Accounts Payable
To solve the complex challenges in accounts payable OCR, automation requires more than just a basic OCR engine. It requires intelligence, flexibility, and seamless integration—and that’s exactly what Unstract delivers.
What is Unstract?
Unstract is a no-code, AI-powered intelligent document processing (IDP) platform built to handle the toughest document workflows across finance, logistics, insurance, and more. For AP teams, it provides a transformative solution for processing invoices, vendor records, remittances, and airway bills.
With Unstract, AP departments can:
Extract structured data from scanned or digital documents
Handle highly varied vendor formats without templates
Deploy workflows as REST APIs for integration with ERP systems
Include human-in-the-loop (HITL) reviews for compliance
Whether you’re a startup scaling operation or an enterprise drowning in invoices, Unstract scales with your document processing needs.
Core Capabilities: Seamless Integration of AI Components
Unstract combines multiple AI technologies into a unified automation pipeline:
Component
Role in AP Document Processing
LLMs
Context-aware extraction of fields from raw text
LLMWhisperer OCR
Layout-preserving text extraction from scanned documents
Embeddings
Organize document data in vector space for similarity matching
Vector DB
Retrieve relevant document sections using RAG techniques
API Deployment
Deploy workflows as REST APIs for seamless integration
Unstract makes it effortless for even non-technical finance teams to automate AP workflows without writing code.
Why Unstract Is Built for Accounts Payable
Here’s how Unstract directly addresses common AP challenges:
Challenge
How Unstract Solves It
Diverse vendor formats
LLMs process natural language instead of relying on templates
Poor scan/image quality
LLMWhisperer uses OCR that retains structure and layout fidelity
ERP integration
API deployment enables plug-and-play connections with systems
Manual verification
Built-in HITL lets AP teams review flagged fields easily
Long, complex documents (e.g., AWBs)
Embeddings + vector DB enable context-based chunk retrieval
Unstract not only digitizes your accounts payable documents, it also understands them. It’s no longer just about extraction—it’s about actionable, structured data that flows directly into your downstream systems.
Whether you’re processing hundreds or millions of AP documents, Unstract is the best OCR software for accounts payable when scalability, accuracy, and ease-of-use are non-negotiable.
What is LLMWhisperer & Why It Matters
In the modern accounts payable automation landscape, OCR is no longer a luxury—it’s a necessity. Yet not all OCR tools are built to handle the real-world messiness of scanned documents, inconsistent layouts, or poor-quality faxes. That’s where LLMWhisperer steps in as a game-changing component of the OCR for accounts payable ecosystem.
LLMWhisperer: A Layout-Preserving OCR Engine
At its core, LLMWhisperer is a general-purpose OCR engine built to extract text from documents while preserving structure and layout—a critical need for finance teams working with invoices, credit memos, remittance slips, vendor onboarding forms, and more.
Unlike traditional OCR tools that simply strip out raw text, LLMWhisperer:
Captures tabular data (including cell alignment and headers)
Preserves checkbox states (important for forms and selection-based documents)
Detects and extracts handwriting in fields like signatures or dates
Maintains visual structure—columns, rows, labels, and indentation are retained in the output
This level of fidelity is essential in accounts payable OCR software, where misplaced totals, misread line items, or scrambled headers can lead to real financial errors.
Important Clarification: Not an LLM
Despite the name, LLMWhisperer is not a Large Language Model, and it doesn’t use LLMs to interpret or extract data.
It is not AI-based
It does not perform any semantic reasoning
It does not classify or label data
Instead, it plays a pre-processing role in the automation pipeline, passing its layout-preserved output to actual LLMs (like GPT or Claude) used by intelligent document processing platforms such as Unstract. In other words, LLMWhisperer “reads” the document, and the LLM “understands” and extracts data from that reading.
Why It Matters in Accounts Payable Workflows
In AP workflows, document structure matters just as much as content. Consider these examples:
Document Type
What Needs Layout Preservation For OCR Accuracy
Invoices
Itemized line tables, tax columns, and totals
Remittance Advice
Reference numbers aligned with invoice numbers and amounts
Vendor Onboarding Forms
Checkboxes, handwritten TINs, and field boundaries
Credit Memos
Segmented data blocks and calculations
Without preserving this structure, OCR outputs become untrustworthy—and trust is everything in finance.
That’s why LLMWhisperer is a foundational layer in modern OCR accounts payable pipelines. It bridges the gap between raw document scans and intelligent data extraction. It ensures that what’s fed into the LLM is clean, readable, and structurally accurate.
Summary: Why Use LLMWhisperer for AP OCR
No LLMs involved — purely deterministic and fast
Works well with poor-quality scans, faxes, and rotated images
Retains spatial layout and field grouping
Ideal for pairing with LLMs in accounts payable OCR software
By using LLMWhisperer in your OCR for accounts payable setup, you’re not just digitizing documents—you’re setting the stage for accurate, structured, and reliable automation that drives down costs, improves processing speed, and enhances financial control.
Showcase: Testing LLMWhisperer OCR
When it comes to OCR for accounts payable, most tools fail when faced with skewed scans, handwritten entries, checkbox forms, or dense tables. That’s exactly where LLMWhisperer sets itself apart. In this section, we’ll explore its OCR capabilities using two real-world tests—one via its Playground UI, and another using API integration with Postman.
Method 1: LLMWhisperer Playground – Testing a Poorly Scanned Remittance Form
To understand how well LLMWhisperer handles real-world challenges, we first tested a remittance form that was:
Tilted at a 35–40° angle
Scanned with poor contrast
Contained handwritten entries
Included multi-column tables and dense numeric data
Successfully deskewed; content aligned and readable
Handwriting
Extracted clearly, especially dates and short notes
Table Extraction
Multi-column tables flattened logically with proper alignment
Data Loss
Zero loss – every table cell and form field retained
Layout Preservation
Structure retained: columns, rows, headings, and sub-fields intact
This kind of performance is critical in accounts payable OCR, where even one missed invoice line or altered total can throw off payment workflows.
Method 2: Calling LLMWhisperer API via Postman – Vendor Form
For developers and automation teams, LLMWhisperer can also be accessed via REST API. Let’s walk through testing a complex vendor onboarding form, which includes:
Generate your LLMWhisperer API Key after logging into Unstract.
Open Postman (web or desktop).
Configure your request as follows:
POST Request
URL: https://llmwhisperer-api.us-central.unstract.com/api/v2/whisper Headers: unstract-key: Body: Type: form-data Key: files Value: (Upload your vendor PDF file)
Click Send and wait for the response.
Check the output (vendor_form_response.txt).
API Output:
Field
Result
Layout with thick grid lines
Lines had no effect on extraction clarity
Checkboxes
Presence and state captured accurately
Field Labels
Detected and associated with the correct values
Micro-sections
Parsed cleanly with correct separation of blocks
Output Format
Clean plain text, preserving logical flow of the form
Content Loss
Zero – even edge-cropped or obscured labels were retained
This test confirmed that LLMWhisperer is not just for clean documents. It thrives on dirty scans, over-designed forms, and layout-heavy PDFs, making it one of the best OCR software for accounts payable.
Why This Matters
In accounts payable operations, documents rarely arrive in pristine digital form. They’re faxed, photographed, watermarked, rotated, even scribbled on. If your OCR tool can’t handle that reality, you’re left manually correcting data—killing the automation benefit.
LLMWhisperer ensures that:
All relevant data reaches the AI layer without noise
No training, layout mapping, or templating is required
You always get clean, structured, and reliable OCR output
Whether it’s invoices, remittance advices, or vendor forms, LLMWhisperer is built to be the OCR engine you can trust in accounts payable automation workflows.
Get Up and Running with Unstract in Just 3 Minutes
Prompt Studio Setup for AP Extraction
One of the core strengths of Unstract as an intelligent document processing platform is Prompt Studio—a no-code interface designed to help you extract structured data from complex, multi-format business documents like invoices, remittance advices, and air waybills. With just a few clicks, you can turn unstructured PDFs into clean JSON output, ready for system integration.
Step-by-Step Setup:
Step 1: Access Prompt Studio
On the Unstract dashboard, click Prompt Studio from the left sidebar. This opens the visual interface where you’ll build your prompt-based extraction logic.
Step 2: Create a New Project
Click New Project and fill in the basic details:
Field
Example
Tool Name
Accounts Payable Prompt Studio
Author / Org
Tarun Singh / [Your Org Name]
Description
Extracting structured data from AP documents like air waybills
Optionally, you can add an icon or company logo to personalize your project.
Click Save to create the project.
Step 3: Upload Sample Document
Click Manage Documents and upload a sample air waybill PDF (e.g., airway_bill.pdf). This is a typical AP document containing:
Multi-column layouts
Handwritten fields
Dense shipping and financial data
This document provides an ideal testing ground to showcase the capability of an accounts payable OCR software like Unstract.
Step 4: Add Field-Level Prompts
Click Add Prompts and begin defining the fields you want to extract. Here’s a sample list of prompts and their natural-language instructions:
{
"shipper_name": "Extract the full name of the shipper. Example: 'Simon Jones'.",
"shipper_address": "Extract the complete address of the shipper, including street, city, state, postal code, and country.",
"shipper_account_number": "Extract the shipper's account number. Example: 'HY73221'.",
"consignee_name": "Extract the full name of the consignee. Example: 'Rogen Gates'.",
"consignee_address": "Extract the full address of the consignee including street, city, postal code.",
"issuing_carrier_agent_name": "Extract the name of the issuing carrier's agent.",
"issuing_carrier_agent_city": "Extract the city of the issuing carrier's agent.",
"agent_iata_code": "Extract the IATA code of the agent.",
"agent_account_no": "Extract the account number of the agent."
}
Click Run, and within seconds, Prompt Studio will display the output in structured format—allowing you to preview the extracted data. This is what makes Unstract’s no-code intelligent document automation stand out: clean, semantic extraction, no code required.
Step 5: Export as a Reusable Tool
Once you’re happy with the outputs, click Export as Tool to convert your prompt project into a reusable document extraction tool. This is now ready to be deployed in production workflows.
Deploying Workflow as API & Testing in Postman
With your document extraction tool built in Prompt Studio, it’s time to integrate it into a fully working Accounts Payable OCR workflow, accessible via API.
Step 1: Deploying the Workflow
A. Navigate to Workflows
Go to the Workflows tab on Unstract’s dashboard and click New Workflow.
Field
Value
Workflow Name
Accounts Payable OCR Workflow
Description
Extracting structured data from AP documents like air waybills
Click Create Workflow.
B. Add Your Prompt Tool
Drag and drop the previously exported tool (from Prompt Studio) into the workflow canvas.
C. Set Up API Input & Output
Configure two essential connectors:
API Input – Accepts PDF documents via HTTP request
API Output – Returns clean structured JSON output to client applications
Click Save, then click Deploy API to launch your production endpoint.
Step 2: Accessing the API via Postman
You now have a live endpoint to perform OCR for accounts payable documents programmatically.
A. Locate API Details
In Unstract, open API Deployment
Copy the API Endpoint URL
Click the 3-dots menu > Manage Keys > Copy your API Key
Now use the status_api URL in a GET request to retrieve the final structured response.
D. View Final Output
Your final airway_bill_response.json will contain structured key-value pairs.
This makes Unstract one of the best OCR software for accounts payable, offering:
Real-time automation
Zero manual intervention
Highly accurate output
No-code deployment
Why This Workflow Matters
Setting up Unstract in this way enables any business to:
Automate vendor form processing, air waybill validation, invoice digitization, or credit memo tracking
Replace error-prone manual entry with a reliable accounts payable OCR pipeline
Maintain accuracy, structure, and speed—even with messy scans or handwritten fields
The result? Fewer delays, faster approvals, and happier vendors—all powered by a scalable intelligent document processing platform.
Conclusion
In today’s fast-moving finance environments, accounts payable OCR is no longer a luxury—it’s a necessity. The sheer volume of invoices, remittance forms, and vendor-related documents demands automation that is not only fast but also accurate, reliable, and easy to integrate.
Traditional OCR tools may offer basic digitization, but they often fall short when faced with real-world challenges: scanned documents at odd angles, handwritten sections, checkbox fields, inconsistent layouts, or multilingual inputs. They require manual template creation, rigid rule-based setups, and extensive training—none of which scale well.
That’s where Unstract sets a new benchmark.
This AI-powered intelligent document processing platform goes beyond standard accounts payable OCR software. With a no-code Prompt Studio, a robust workflow engine, and seamless API deployment, Unstract brings enterprise-level document automation within reach for every finance team.
Whether you’re handling a poorly scanned remittance form, a vendor onboarding PDF with dozens of fields, or a multi-column air waybill, Unstract processes each with clarity and speed—no templates, no training data, no rework.
Why Unstract is the Ideal OCR Solution for Accounts Payable:
Feature
Benefit
No-code Interface
Finance teams can deploy without IT support
Layout-Preserving OCR
Ensures fidelity in complex financial documents
Structured JSON Output
Easy ERP/finance system integration
LLM-Powered Extraction
Context-aware, natural-language field recognition
Scalable API Workflows
Real-time automation across multiple AP scenarios
In short, if you’re searching for the best OCR software for accounts payable, Unstract delivers on every front—accuracy, speed, transparency, and most importantly, control.
As businesses modernize their finance operations, Unstract stands out as a future-ready solution. It’s not just OCR for accounts payable, it’s intelligent, structured, human-grade document understanding—designed for today, built for tomorrow.
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