9 Best OCR Software Applications for Invoices

Optical character recognition (OCR) is a technology that enables the conversion of text in images, PDFs, and scanned documents into machine-encoded text data. It uses pattern matching or feature extraction to identify and extract printed or handwritten text. First, text is isolated from the background, and then either compared to known characters or analyzed for the unique characteristics of each character, such as the shape, stroke patterns, pixel distributions, or other visual features that distinguish one character from another.

Modern OCR solutions use advanced technologies like machine learning models trained on large datasets of labeled text images to improve the accuracy of character recognition and extraction. Other solutions use generative AI, which provides even higher accuracy than machine learning models due to its ability to understand context. Generative AI solutions also are easier to use since, unlike machine learning-powered OCR, they don’t require any model training in order to recognize custom data fields and highly varied invoice layouts.

OCR can be applied to all types of physical and digital documents, including invoices, to reduce manual data extraction, improve data accuracy, and increase operational efficiency. With so many OCR software applications available, how do you choose the best one for your needs? Here are nine of the best OCR tools for invoice data extraction and their pros and cons to help you narrow down your choices. 

Instabase AI Hub

Instabase AI Hub automates data processing of structured, semi-structured, and unstructured documents, enabling organizations to analyze and act on their data faster and more efficiently. 

Powered by cutting-edge generative AI and large language models (LLMs), AI Hub eliminates the need for technical expertise, as it understands and responds to natural language prompts. This user-friendly approach enables anyone to effortlessly extract data from documents in any way they want. Users simply tell the AI what data they need to extract and how it should be formatted. Unlike OCR solutions that use machine learning, AI Hub doesn’t require any model training. It’s highly accurate and all of its capabilities are available out of the box.

AI Hub offers many different AI applications for a variety of use cases. For invoice data extraction, organizations can use the Converse and Build apps, depending on the volume of invoices they need to process and how often they need to extract data.

Converse 

Converse is ideal for occasionally extracting data from a small number of invoices. 

After uploading your documents to Converse, simply tell Converse the data that you want to extract, whether it’s all the data displayed or only select fields. Because Instabase excels in handling unstructured data, users can extract data that other OCR tools struggle with, such as handwritten notes, signatures, and checkboxes.

While Converse is excellent at data extraction, it can also be used for additional data processing needs since it provides users with an unparalleled amount of flexibility to interact with their data. Users can also convert currencies, format and standardize data, and even perform analyses on their invoices.

Pros	Cons
Users can easily customize the data they want to extract and how it’s extracted by using natural language.	Converse is not built for workflow automation, so every task needs to be manually prompted.
Converse supports additional data processing such as refinement, calculations, and analysis.	Only API integrations are currently available.
With no model training or creation of custom fields or templates required, users can start using Converse out of the box — no implementation or engineering required.
Users can extract data from international invoices, as Converse supports over 160 languages and can convert currencies.

Ideal Use Cases and Users

Build 

The Build application enables users to create automated, AI-powered workflows without coding or machine learning expertise. Through a natural language interface, users can design custom workflows for document processing that include tasks like data extraction, data validation, enrichment from databases, human review, and more.

Build allows users to define the specific fields and data they want to extract from invoices, such as vendor name, invoice number, line items, and totals. Because Instabase’s AI understands context, it’s able to intelligently map fields to the correct data shown in the invoice without users having to manually annotate the document.

Once users have created their automated workflow, it can be integrated with downstream systems, like enterprise resource planning (ERP) and accounting software, via APIs and deployed in cloud or on-premise environments.

Pros	Cons
Build is highly customizable and works based on natural language prompts, enabling anyone to use it.	Users may need to adjust their prompts to achieve their desired results.
Build supports additional data processing capabilities that ensure extracted data is compatible with your end systems or ready for the next step in your workflow. For example, you can convert currencies, format dates, and verify whether invoices are sent from customers in your database.	Some advanced functions, such as validation and integrations, require Python.
Build uses OCR and proprietary and third-party AI models to ensure customers have access to the latest innovations and highly accurate results.	Only API integrations are currently available.
The app automatically flags issues and loops in humans for review when necessary.
Users can share their app with anyone in their organization for standardized invoice processing.

Ideal Use Cases and Users

Pricing

Rossum

Rossum automates data capture and validation by leveraging AI to automatically extract data from invoices without the need for user-defined templates or rules. Instead, it uses self-learning neural networks to mimic how a human would search for and extract information from an invoice document. It can automatically identify and capture data like vendor name, invoice number, line items, and totals from invoices in various formats.

Pros	Cons
Rossum can handle diverse invoice formats out of the box using its pre-trained AI models.	The initial setup process can be time-consuming and challenging despite its user-friendly interface.
Users can extract data from the fields that Rossum provides or add custom fields.	Rossum has a narrow range of use cases, as it’s limited to transactional documents such as invoices, purchase orders, and bills of lading.
Data can be validated against predefined rules and third-party databases to ensure accuracy.	It uses its own proprietary large language model, which means customers lose out on innovations in the market and are completely reliant on Rossum to keep up with the rate of innovation.
The extracted data can be integrated into downstream systems like ERPs, accounting software, and payment platforms via APIs for further processing.	Although Rossum’s LLM is already trained on millions of documents, companies still need to train the LLM with their specific documents in order to achieve higher accuracy (over 85%).
Rossum automatically flags invoices that require human review.	Rossum supports a limited number of languages, and its performance degrades when working with non-English text.
It detects duplicate invoices.

Ideal Use Cases and Users

Pricing

Tungsten OmniPage Ultimate

OmniPage Ultimate uses OCR technology to convert scanned, PDF, and digital camera images of invoices into editable and searchable formats. The software integrates with various scanners, multifunction printers (MFPs), and mobile devices for document capture and conversion.

Pros	Cons
OmniPage Ultimate can automatically detect and process invoices in over 120 languages.	It struggles to accurately extract text when the scan quality is low.
Extracted invoice data can be exported to various formats like PDFs, Microsoft Office documents, HTML, and more.	The interface feels cluttered and unintuitive.
It supports scheduling and unattended batch processing of invoices, enabling automated invoice digitization and data extraction workflows.	As a legacy solution, OmniPage Ultimate hasn’t kept up with technological innovations like AI, which means that users won’t benefit from a solution that’s keeping up with the pace of innovation.
The software offers unique features like auto-redaction via keywords and bookmarking, and form data collection, which are not available in the standard edition.	It’s only available as desktop software and is exclusively compatible with Windows operating systems — not Mac.

Ideal Use Cases and Users

Pricing

ABBYY Flexicapture for Invoices

ABBYY FlexiCapture for Invoices is an intelligent data capture and extraction solution that also utilizes AI, natural language processing, and machine learning. Built on the ABBYY FlexiCapture platform, it has additional capabilities like predefined settings, validation rules, and database look-up that are tailored to invoice processing. 

Pros	Cons
FlexiCapture’s auto-learning technology improves data extraction accuracy by learning from users’ actions and decisions during document processing.	Despite FlexiCapture’s auto-learning capabilities, users may need to train it before or during document processing to improve image and field detection.
The software automatically validates extracted data against predefined rules, country-specific regulations, and company databases.	FlexiCapture struggles with accurately extracting data from invoices with poor image quality, complex layouts, or non-standard formats, leading to errors or missed data fields.
FlexiCapture flags invoices that can’t be automatically validated for human verification.	There’s a steep learning curve, especially when setting up new document types and training the system.
It recognizes over 200 languages.

Ideal Use Cases and Users

Pricing

DocuClipper

DocuClipper specializes in OCR for financial documents like invoices, receipts, bank statements, and credit card statements. While it’s a focused solution that’s good for OCR, DocuClipper lacks the additional capabilities that are usually needed for data processing. Most notably, it doesn’t have any validation capabilities, which means users need to manually double check all extracted data.

Pros	Cons
DocuClipper integrates with popular accounting tools like QuickBooks Online, Xero, and Quicken, allowing users to map and import extracted data into their accounting platforms.	The system struggles to accurately extract data from invoices that don’t follow a typical invoice format.
The software provides an API for programmatic import of invoices, extraction of data fields, and retrieval of converted data in various formats.	All DocuClipper offerings, except for the Enterprise plan, limit the number of pages that can be processed each month.
DocuClipper claims to achieve over 97.5% accuracy for invoice data extraction.	DocuClipper only offers monthly subscription plans based on page volume, which may not suit companies with irregular extraction needs or inconsistent document processing volumes.
	When DocuClipper detects multiple accounts within an invoice or statement, it downloads a separate file for each account by default. Users have to manually uncheck an option to consolidate data into a single file.

Ideal Use Cases and Users

Pricing

DocParser

DocParser is a template and rule-based OCR solution. While it can be customized to extract data from any type of document, it offers pre-built templates and parsing rules for several document types, including invoices, purchase orders, and bank statements. Users can also create parsing rules and templates specific to their invoice formats without any coding.

Pros	Cons
DocParser uses zonal OCR, which allows users to extract data from specific areas of a document.	Since data extraction is based on templates and parsing rules, setup can be time-consuming if you work with many different invoice formats.
It offers a confidence score for extracting invoice totals that’s based on whether the net and tax amounts add up to equal the total.	While prebuilt parsing rules exist for common invoice fields, users need to manually create parsing rules for other fields they want to extract.
DocParser provides several options for exporting data, including webhooks and an API for real-time data delivery, as well as integrations with various cloud storage services and applications.	There may be a steep learning curve for setting up parsing rules and templates for different invoice formats, depending on your technical abilities.
The system supports over 40 languages.	DocParser struggles with accuracy when the size and cells of tables vary across documents.
	You’re limited to uploading documents with up to 30 pages.
	DocParser only supports several file types: PDF, Word, PNG, JPG, and TIFF.

Ideal Use Cases and Users

Pricing

Nanonets

Nanonets uses AI and machine learning to automate the extraction of data from various types of documents, including invoices. Using pre-trained models, Nanonets can automatically extract a limited number of fields from invoices, regardless of their layout or format.

Pros	Cons
Nanonets allows users to create custom fields and models for data extraction without requiring technical skills.	Creating custom fields for a pre-trained model requires users to manually annotate about 50 documents.
It integrates with over 100 applications, including popular ERPs and accounting software like SAP, and supports custom integrations through APIs and webhooks.	Developing and training a custom invoice model is time-consuming, as it requires manual annotation of at least 10 documents and two to eight hours of training. More complex documents may need additional training examples.
Nanonets offers post-processing tools like data formatting and validation.	Nanonets can only process up to 20 pages per minute unless users have an enterprise account.
The system supports over 40 languages.	The software lacks advanced output customization features, which presents a significant limitation for users with specific requirements for formatting or presenting extracted invoice data for downstream processing.

Ideal Use Cases and Users

Pricing

Docsumo

Docsumo is a document AI platform that offers pre-trained models for specific types of documents, including invoices. Additionally, users can train custom machine learning models to handle specific invoice formats or layouts. While it’s mainly built for data extraction, Docsumo also offers some additional document processing capabilities like classifying and splitting documents. 

Pros	Cons
Users can create custom models and fields without requiring technical skills.	Training Docsumo’s models to adjust to varying document formats can be time-consuming.
Machine learning enables Docsumo’s models to learn and improve accuracy as users adjust and correct the model’s output.	Training a custom model requires annotating at least 20 document samples.
Docsumo provides additional automation features, including document classification, document splitting, reviewer assignment for exceptions, and alerts for discrepancies or areas needing manual review.	Docsumo’s data validation capabilities require knowledge of Excel functions.
It offers integrations with popular platforms like Xero, Quickbooks, and Salesforce, and it can connect with many other applications via API, webhooks, and Zapier.	Document classification requires model training.
	Initial setup can take a long time if you need to create a lot of custom fields or custom models for your invoices.

Ideal Use Cases and Users

Pricing

Klippa DocHorizon

Klippa DocHorizon is an AI-powered document processing solution that can extract data from over 50 types of documents out of the box, including invoices. It uses both OCR and machine learning to extract and even classify line items from invoices. Klippa also provides users with the ability to build automated workflows for processing their documents.

Pros	Cons
Klippa allows users to add custom data fields for extraction.	Custom data extraction requires extensive model training, with at least 500 annotated documents needed.
It can identify fraudulent invoices and detect duplicate submissions.	The accuracy of Klippa’s extraction lags behind that of its competitors.
Klippa offers integrations with accounting and ERP applications, including Xero and Quickbooks, and provides an API and webhooks.	Klippa offers limited data extraction and workflow customization options, which can be problematic for businesses with unique invoices and requirements.
The system can classify invoice line items into over 20 categories.
Klippa supports human in the loop so that users are notified to manually review the output when certain conditions are met.

Ideal Use Cases and Users

Pricing

Unlock the Power of Intelligent Invoice Processing With Instabase

While there are clearly many OCR-powered invoice data extraction solutions available, Instabase AI Hub stands out as the most comprehensive, powerful, and accessible option. By combining OCR with generative AI and large language models, AI Hub goes beyond OCR’s capabilities to deliver unmatched accuracy, automation, flexibility, and ease of use. From seamlessly handling complex document layouts and unstructured data to offering robust data security and compliance, Instabase’s Converse and Build apps are designed to unlock the information trapped in your documents and easily process your data however you like. Further, for organizations that need to extract data from invoices at scale, Build streamlines your invoice processing workflows and drives operational efficiency.