Nanonets Reciept OCR
OCR software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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Pay-as-you-go
Free Trial
Free version unavailable
Small
Medium
Large
- Accommodation and food services
- Retail and wholesale
- Agriculture, fishing, and forestry
What is Nanonets Reciept OCR
Nanonets Receipt OCR is an OCR and data extraction product focused on capturing structured fields from receipts and invoices. It is used by finance, accounting, and operations teams to digitize expense documentation and feed extracted data into downstream systems via API or integrations. The product emphasizes template-less extraction using machine learning models and supports custom field definitions and validation workflows for document processing.
Receipt-focused field extraction
The product is designed around common receipt and invoice fields such as merchant, date, totals, taxes, and line items. This focus reduces the amount of manual mapping needed compared with general-purpose OCR tools. It also supports exporting structured outputs (e.g., JSON/CSV) that are practical for expense and AP workflows.
API-first integration options
Nanonets provides APIs that allow developers to embed receipt OCR into web and mobile applications. This makes it suitable for building automated ingestion pipelines and connecting to internal systems. API-based usage can be easier to operationalize than tools that primarily rely on desktop capture or repository-centric document management.
Custom model and labeling workflow
The platform supports training or customizing extraction models using labeled examples, which helps when receipts vary by region, language, or format. Users can define custom fields and validation rules to align outputs with internal accounting requirements. This flexibility is useful when off-the-shelf receipt parsers do not capture organization-specific data points.
Accuracy varies by input quality
As with most OCR, performance depends heavily on image quality, lighting, skew, and receipt print clarity. Low-resolution photos, crumpled receipts, and thermal paper fading can reduce extraction accuracy and increase manual review. Teams should plan for exception handling and human validation for edge cases.
Not a full content platform
Receipt OCR focuses on extraction rather than end-to-end document management features such as records retention, complex routing, and enterprise repository controls. Organizations needing broad ECM capabilities may require additional systems for storage, governance, and enterprise search. This can add integration and administration overhead.
Implementation requires tuning
Achieving consistent results often requires field configuration, sample collection, and iterative model tuning. For organizations without technical resources, API integration and monitoring may be more effort than using a fully packaged capture-and-workflow suite. Ongoing changes in receipt formats can also require periodic retraining or rule updates.
Plan & Pricing
Pricing model: Pay-as-you-go (credit-based) Free tier/trial: $200 in free credits on signup (one-time credit). Invoice OCR: first 100 invoices free (documentation note).
Base prices (credits per run) — core units (from official docs):
- Data Extraction AI — 0.30 credits / run.
- Classification AI — 0.10 credits / run.
- Checkbox Detection AI — 0.10 credits / run.
- Barcode/QR/Signature Detection AI — 0.10 credits / run.
Import / Export / Enrichment (credits / run):
- Import (Email, API, Google Drive, Dropbox, SharePoint, OneDrive) — 0.04 credits / run.
- Custom Integration / ERP / CRM import — 0.15 credits / run (Premium/Enterprise).
- Data Formatting (Date, Numbers, Regex) — 0.02 credits / run.
- Python Block / LLM post-processing — 0.18 credits / run.
- Data Lookup (Google Sheets, MySQL, CSV) — 0.04 credits / run.
- QuickBooks / Sage / Xero lookups/exports — 0.10 credits / run.
- Salesforce / SAP / Oracle / NetSuite / D365 lookups/exports — 0.15 credits / run.
- Data Export to DB/Doc management — 0.04 credits / run.
Model management & add-ons (examples):
- Model Versioning — 50 credits / month (Enterprise availability).
- AI Confidence Scores — 500 credits / month.
- UAT Model — 100 credits / month (Enterprise).
- Whitelabel UI — 200 credits / month (Enterprise).
Discounts / volume options:
- Credits Accelerate (volume prepay for discounted credits; contact Sales for tiers and enterprise pricing).
- Annual commitment discounts available per pricing page.
Notes & examples:
- Billing is computed as sum(block price × number of block runs) + add-on fixed fees; credits are consumed to pay for usage.
- Pricing and structure are usage/credits-based; exact USD equivalent depends on purchased credit packages/discount tiers (contact Sales for custom quotes).
Seller details
Nanonets, Inc.
San Francisco, CA, USA
2017
Private
https://nanonets.com/
https://x.com/nanonets
https://www.linkedin.com/company/nanonets/
