Nanonets Alternative — Start Extracting Data Today, Without Training a Single Model
Nanonets users report that model training takes too long and per-block pricing becomes unpredictable as volume grows. ImageToTable takes the opposite approach: type the column names you want, and AI finds those values on any document — no training, no per-page pricing surprises, no developer required.
5-10s per page · 99% accuracy on printed text · Zero training required
What You Get Switching from Nanonets
Beyond the core extraction capability, here are the features that make ImageToTable a fundamentally different tool — not just a cheaper version of the same thing.
Each of these is a capability where ImageToTable's approach differs from Nanonets' model-training paradigm — not just a feature checkbox.
Nanonets Asks You to Train Models. ImageToTable Asks You to Name Your Columns.
This isn't a minor UX difference — it's a fundamentally different approach to document data extraction. One requires patience and sample preparation. The other gives you results in the time it takes to type a few column names.
The Nanonets Approach: Train First, Extract Later
You annotate samples before extracting anything. Nanonets requires uploading and labeling at least 8-10 verified sample documents per document type. Users report that "the initial training time for the AI model can be quite long" — and every new document format adds to that time.
Per-block pricing creates cost unpredictability. A typical invoice workflow runs 4-6 blocks at $0.02-$0.30 each — ~$2 per document end-to-end. Plus additional fees for formatting, lookups, and premium integrations. Costs that industry analysis describes as "difficult to predict and typically more expensive at most volume levels."
You get raw extracted data — the rest is up to you. Nanonets extracts what's on the document. If you need line-item calculations, category classification, or derived totals, you do that in a spreadsheet after export. It's an extraction tool — not an extraction-plus-intelligence tool.
The ImageToTable Approach: Name It, Extract It
Zero training — you type column names and get results immediately. No sample documents, no labeling, no waiting for a model to train. Type "Invoice Number", "Due Date", "Total" — the visual AI understands what those terms mean and finds the corresponding values anywhere on the document by semantic meaning, not by learned pixel positions. Works from the first upload.
Page-based pricing with a free tier — no per-block arithmetic. Instead of calculating block counts per document, you get straightforward page-based plans. Know what you'll pay before you upload. No surprises when a document needs an extra processing block.
The AI computes, infers, and structures during extraction — not after. Need line totals calculated from quantity and unit price? Add a Computed Column like "Line Total (Qty × Unit Price)" — the AI does the math as it extracts, and you get calculated answers, not raw numbers to post-process. Need to categorize expenses by type? Add an Inferred Column like "Category (options: Meals/Transport/Office)" — the AI reads the document content and classifies it, even though no "Category" label exists on the page.
Same Task, Two Tools: A Realistic Comparison
You've got 50 vendor invoices in different formats. You need Invoice Number, Vendor Name, Invoice Date, Subtotal, Tax, and Total in a single spreadsheet. Here's how each tool gets you there.
1 With Nanonets
Step 1: Upload 10 sample invoices. Manually label Invoice Number, Vendor, Date, Subtotal, Tax, and Total on each one in the training interface — verifying each field position.
Step 2: Wait for model training to complete. Depending on document complexity and queue, this can take hours.
Step 3: Deploy the trained model. Process your 50 invoices. Review results for accuracy — if a new vendor format appears, you may need to add it to training and retrain.
Step 4: Export extracted data. If you need line-item calculations or category classification, open Excel and add formulas manually.
1 With ImageToTable
Step 1: Type six column names: Invoice Number | Vendor Name | Invoice Date | Subtotal | Tax | Total. That's it. No samples, no labeling.
Step 2: Upload all 50 invoices — PDFs, scans, screenshots — in one batch. The AI processes them immediately with the column names you defined. Processing takes 5-10 seconds per page.
Step 3: Download one clean Excel file where each row is an invoice and columns match exactly what you named. Export in XLSX, CSV, or JSON — the data is standardized and ready to use.
Optional: If you want line totals calculated, add a Computed Column. Need expense categories inferred? Add an Inferred Column. Done during extraction — not in a separate spreadsheet session.
When ImageToTable Fits — and When Nanonets Does
Different tools for different needs. Here's an honest breakdown of where each one excels — so you pick based on your actual workflow, not marketing claims.
ImageToTable Is the Better Fit When
You need to start extracting today. No training cycle, no sample preparation, no deployment. Type column names, upload documents, get results. The learning curve is measured in minutes, not days.
You need more than raw data extraction. Computed Columns let you calculate during extraction (Line Total = Qty × Unit Price). Inferred Columns let the AI classify and derive information not written on the document. These eliminate post-processing spreadsheet work entirely.
You need to collect documents from external people. With Collection Link, you generate a shareable URL — vendors, employees, or clients open it, enter a verification code, and upload files directly into your processing queue. No registration, no login, no training anyone.
You want editable Word output with original formatting. Beyond structured Excel data, the To Word mode preserves the document's visual layout — text, tables, stamps — in an editable Word file. Nanonets doesn't offer this.
Your team has no dedicated developers. ImageToTable works entirely in the browser — no API integration, no coding, no IT setup. Google Sheets users can use the add-on to extract directly into spreadsheets.
Nanonets Is the Better Fit When
You're running a high-volume API pipeline at enterprise scale. If you process 10,000+ documents per day through automated API calls with routing, Nanonets' infrastructure and workflow orchestration are purpose-built for this. ImageToTable is optimized for interactive, human-driven workflow — not unattended API firehoses.
You need deep ERP integration with approval workflows. Nanonets connects directly to SAP, QuickBooks, Xero, NetSuite, and Salesforce — with built-in approval routing, three-way matching, and automated posting. If your process lives inside these systems, Nanonets' native integrations save you the middleware step.
You need enterprise compliance and deployment controls. SOC 2 Type II, HIPAA BAAs, SSO/SAML, private cloud deployment, data residency controls, audit logs streaming to SIEM — Nanonets has enterprise security infrastructure that ImageToTable does not match. If your org's compliance requirements demand these, Nanonets is the right tool.
You need fine-tuned models for very niche document types. Nanonets' 300+ pre-trained models cover specialized formats (insurance claims, healthcare forms, logistics documents). If you process a high volume of a single niche document type and have the resources to fine-tune a dedicated model, Nanonets' training pipeline is the right approach.
You need built-in document classification and routing. Nanonets auto-classifies incoming documents by type and routes them to the correct processing workflow. If your intake channel receives mixed document types that need automatic sorting before extraction, Nanonets has this built in.
Frequently Asked Questions
Does ImageToTable require model training like Nanonets?
No. ImageToTable uses zero-training column-name extraction — you type the column names you want (like "Invoice Number", "Date", "Total"), and the visual AI finds those values anywhere on the document by understanding their semantic meaning. There's no sample labeling, no model training cycle, no deployment step. It works from the very first upload, across any document format you throw at it. This is the single biggest architectural difference between the two tools.
How does pricing compare between ImageToTable and Nanonets?
They use fundamentally different pricing models. Nanonets charges per processing "block" — an invoice workflow typically uses 4-6 blocks at $0.02-$0.30 each (~$2 per document), plus additional fees for formatting, lookups, and integrations. ImageToTable uses straightforward page-based plans with a free tier. For teams processing moderate volumes without enterprise workflow requirements, ImageToTable's pricing is simpler and more predictable — you know what you'll pay before uploading, without calculating block counts.
Can I extract document data that isn't explicitly written — like inferring an expense category from a receipt?
Yes, and this is a capability Nanonets doesn't offer. With Inferred Columns, you define a column like "Category (options: Meals/Transport/Office/Other)" — the AI reads the document content, understands the context, and fills in the appropriate category value, even though no "Category" field exists on the document. This works across batch uploads too, so extraction and classification happen in a single pass. Similarly, Computed Columns let you perform calculations during extraction — like "Line Total (Qty × Unit Price)" — so you get calculated answers, not raw data to post-process.
What if I need ERP integrations like the ones Nanonets offers?
This is genuinely where Nanonets has the advantage. If your workflow depends on direct integration with SAP, QuickBooks, Xero, NetSuite, or Salesforce — with automated approval routing, three-way matching, and posting — Nanonets' native ERP connectors and workflow orchestration are the right tool for that job. ImageToTable focuses on getting data from documents into structured spreadsheets (Excel, CSV, JSON) quickly and accurately. You can import those files into your ERP, but ImageToTable doesn't offer the native, bidirectional ERP sync that Nanonets does. If ERP integration is your primary requirement, Nanonets may be the better fit — and we'll say that honestly.
Does ImageToTable offer API access for automated workflows?
ImageToTable is primarily a browser-based tool designed for interactive use — upload documents, define columns, and export results through the interface. The Google Sheets add-on provides a semi-automated workflow for spreadsheet users. For fully automated API-driven pipelines at very high volumes (thousands of documents per day with programmatic submission and routing), Nanonets' API infrastructure is a better fit. ImageToTable is optimized for teams that process documents in batches through a human-driven workflow — not unattended, high-frequency API calls. Check the blog posts linked below for frameworks to decide which approach matches your volume and workflow.