5 Best AI Tools for Extracting Data from Images

Until recently, many business teams had only one practical option for feeding data from scanned documents, screenshots, and images into their systems: manual data entry. The work was slow, repetitive, and susceptible to errors, but it was often the only way to move data into finance systems, customer systems, or reporting workflows.

Thankfully, this approach is no longer necessary today. AI-driven image extraction can pull the same fields faster and with greater consistency than manual entry, making rekeying an avoidable drain on skilled professionals’ time. The decision has shifted from whether to automate to which tool can meet your accuracy, control, and scale requirements.

The market is crowded, and most platforms sound similar on paper. What separates them in practice is how well they handle real-world variability: imperfect images, mixed layouts, line-item tables, handwritten notes, and constant format changes. Below, we’ll break down the limitations of traditional approaches and what to look for in modern AI extraction tools before comparing the leading options.

Challenges in Traditional Image Data Extraction

Traditional image extraction typically relies on manual entry or Optical Character Recognition (OCR) paired with rigid rules. These methods can work in narrow, controlled cases, but they struggle when document formats vary and volumes spike.

Accuracy Issues Create Downstream Errors 

OCR frequently misreads characters in blurry scans, low-contrast images, or skewed photos. Small misreads like confusing “0” and “O” or dropping a decimal can cascade into incorrect postings, reconciliations that don’t match, and rework during review.

Volume and Operational Bottlenecks

When document volumes increase, manual data extraction becomes a throughput constraint. Many legacy tools also struggle under load, creating backlogs that delay payment cycles and reporting timelines.

Layout Variability Breaks Templates

Invoices and reports rarely follow a consistent structure. When fields shift location, tables change shape, or attachments get added, template-based extraction becomes brittle and exceptions spike.

Template Sprawl Doesn’t Scale

Even if templates work for one document type or a small supplier set, coverage becomes a maintenance problem as you add new vendors, regions, and document categories, each requiring new rules, testing, and ongoing updates.

What Makes AI Effective for Image Data Extraction?

Template-driven extraction looks for data in predefined places. AI-powered extraction uses computer vision and machine learning to interpret structure and meaning, which makes it more resilient to layout changes and messy inputs. The best tools do more than just “read text” — they understand which values matter, how fields relate to each other, and what constitutes a valid result.

Here are the capabilities that separate enterprise-grade AI tools from basic OCR:

Contextual Understanding of Documents – The tool recognizes concepts like invoice number, total, tax, ship-to, remit-to, and line items even when their placement varies between suppliers or document types.

Robust Handling of Complex Layouts – Strong tools extract from tables, charts, and mixed content, and they preserve line-item structure rather than collapsing everything into a text dump.

Handwriting and Annotation Tolerance – Real documents often include handwritten notes, stamps, signatures, and markups. A practical extraction tool must be capable of handling those artifacts without corrupting core fields.

Top AI Tools for Image Data Extraction

1.  Savant Vision Agent

If you want an easy-to-use, enterprise-grade AI tool for effective image data extraction at scale, Savant’s Vision Agent is a great option. This agentic AI tool uses vision-first extraction to turn unstructured visual content like scans, images, and PDFs into clean, ready-to-analyze data. It’s adept at interpreting invoices, reports, charts, graphs, and more, even when formatting shifts across vendors, regions, or scanning quality

Key Features

• Converts diverse visual formats, including tables, charts, graphs, and handwritten and printed text, into structured, machine-readable data
• Connects into existing enterprise data sources via prebuilt connectors so teams can pull documents in and push extracted data downstream with less integration lift
• Supports high-throughput processing for repeatable document workloads (e.g., invoice batches, statement packets) while keeping outputs consistent across formats
• Designed for governed workflows where you can review exceptions and maintain traceability back to the source document

Pros

• Strong fit for enterprise use cases where document variability is the norm (multiple entities, regions, and formats)
• >98% accuracy in extracting structured data from unstructured sources
• Audit-ready outputs and human oversight make it suitable for highly regulated industries
• Comes as a part of a broader analytical platform, enabling workflows that go far beyond just extraction

Cons

• May be overpowered for small teams that only need lightweight, occasional extraction

Pricing

Pricing for Savant’s platform starts at $3,000 per year for the Pro subscription, ideal for teams looking to automate analytics workflows, including image and PDF extraction. For details about the Enterprise subscription pricing, request a quote here.

2. Rossum

Rossum is a top AI-powered Intelligent Document Processing (IDP) platform. Rossum makes document processing easier by extracting and validating data from complex business documents, including invoices and insurance forms, without relying on per-supplier templates. 

Key Features

• Layout-adaptive extraction aimed at common enterprise document types (invoices, forms, operational documents)
• Validation and review workflows so teams can correct edge cases and standardize outputs before posting to downstream systems

Pros

• No manual configuration for each new document type
• Validation layer helps reduce downstream rework when document quality is inconsistent

Cons

• Meaningful value depends on integration + workflow configuration — it’s not just plug and play
• Slight learning curve involved

Pricing

Rossum does not have any publicly listed pricing plans. 

3. Nanonets

Nanonets is a no-code, AI-powered document processing platform that automates the extraction of structured data from images, scans, and other file formats. It allows easy data extraction and quick integration with other systems, helping businesses streamline workflows and reduce manual work.

Key Features

• Automatically captures structured data from images of business documents like invoices, receipts, purchase orders, and forms
• Supports end-to-end workflows that ingest, extract, validate, and route data with minimal manual intervention
• Exports outputs to structured formats (CSV, JSON, XML) or pushes data directly into business systems and analytics tools

Pros

• Leverages machine learning to handle documents without fixed rules or templates
• Includes workflow controls for review and exception handling, which helps in higher-volume processes

Cons

More complex workflows usually require configuration, testing, and ongoing tuning to keep accuracy and routing behavior stable as document formats change

Pricing

Nanonets pricing is credit- or usage-driven, with costs tied to the number of block runs. Simpler tasks cost less, while more complex extraction blocks tend to be pricier.

4. Octoparse

Octoparse is primarily a web automation and web scraping platform — it’s strongest when your images and supporting fields live on web portals (vendor sites, marketplaces, internal web apps) and you need to collect that content at scale. It is not an invoice-first extraction system in the way document AI platforms are, so it’s best framed as a capture layer that can feed documents/images into a downstream extraction step.

Key Features

• No-code automation for collecting data from websites, including workflows for navigating pages and extracting content
• Useful when teams need repeatable collection from web sources before running document extraction elsewhere, like grabbing invoice PDFs from a vendor portal

Pros

• Helpful for scaling web-based collection without building custom scrapers
• Integrates well with your existing databases and business apps

Cons

• Not purpose-built for invoice/receipt understanding from images; you’d typically still need a document extraction tool after collection

Pricing

Octoparse offers a free plan with limited features. Paid plans, including the Starter Plan ($29/month), Team Plan ($49/month), and Enterprise Plan, are also available and offer advanced features.

5. Bright Data

Bright Data is a platform for extracting structured data from the public web at scale using APIs, proxy networks, and scraping tools. It lets businesses collect real-time and historical data from websites, including images, text, and metadata, for analytics.

Key Features

• Quickly access structured web data on demand, including real-time and historical, data
• Leverages significant proxy infrastructure to access sites without blocks, CAPTCHAs, or geo-restrictions
• Designed to handle massive data extraction volumes with API delivery in JSON, CSV, NDJSON, and other formats

Pros

• Strong fit for enterprise-scale public web data collection.
• Built to support consistent collection where access constraints, geo-variation, or scale create operational friction.

Cons

• Not purpose-built for converting scanned invoices, receipts, or internal document images into structured fields
• Implementation can require developer time to configure APIs, workflows, and operational controls

Pricing

Bright Data offers modular pricing according to the services you use and how much data is collected. 

Enhance Business Operation With AI-Powered Image Data Extraction

Image-based data extraction can remove a major bottleneck in finance operations, but tool selection should be driven by fit, not marketing claims. The most useful platform is the one that aligns with your document complexity, volume, required integrations, and control requirements — for example, review workflows, auditability, and access governance.

AI extraction can eliminate a meaningful amount of manual rekeying and reduce the late-cycle surprises that show up during close. The right platform will handle real-world document variability, produce consistent structured output, and keep humans focused on exceptions rather than routine data entry. For teams that want to operationalize this quickly, Savant’s Vision Agent is built to turn unstructured images and PDFs into analysis-ready data with governance and exception handling designed for finance workflows.