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deepread

AI-native OCR platform that turns documents into high-accuracy data in minutes. Using multi-model consensus, DeepRead achieves 97%+ accuracy and flags only uncertain fields for Human-in-the-Loop (HIL) review—reducing manual work from 100% to 5-10%. Zero prompt engineering required.

Why use this skill?

Transform your documents into structured data with DeepRead. Get 97%+ accuracy using multi-model consensus and reduce manual data entry by up to 95% with HIL.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/uday390/deepread-ocr
Or

What This Skill Does

DeepRead is an AI-native OCR platform designed to transform raw documents—such as PDFs, invoices, and images—into high-accuracy structured data. By utilizing an advanced multi-model consensus architecture, it achieves upwards of 97% accuracy without the need for manual prompt engineering or complex configuration. The core value proposition lies in its intelligent 'Human-in-the-Loop' (HIL) integration, which automatically flags uncertain data fields for review, effectively reducing the manual workload for document processing by 90-95%. Whether you are processing thousands of invoices or digitizing archival records, DeepRead acts as a production-grade interface that bridges the gap between unstructured physical documentation and clean, actionable JSON data.

Installation

To integrate the DeepRead skill into your OpenClaw environment, ensure you have the OpenClaw agent configured. Use the following command in your terminal:

clawhub install openclaw/skills/skills/uday390/deepread-ocr

Once installed, obtain your API key from the DeepRead Dashboard. Set this key as an environment variable in your system: export DEEPREAD_API_KEY="sk_live_your_key_here". Finally, ensure your clawdbot.config.json5 file has the DeepRead entry enabled to finalize the setup.

Use Cases

  1. Financial Document Automation: Automatically parse invoice totals, line items, and tax identifiers for integration into accounting software.
  2. Legal and Medical Record Digitization: Convert legacy physical archives into searchable, structured markdown for internal databases.
  3. Supply Chain Documentation: Extract shipping manifest data, tracking numbers, and delivery dates from scanned manifests to feed into logistics management systems.

Example Prompts

  1. 'Process the file located at ./invoices/january_report.pdf and extract the total amount and vendor name into JSON format.'
  2. 'Run the DeepRead OCR on the scanned shipment receipt in my downloads folder and alert me if there are any low-confidence fields that need my review.'
  3. 'Read the document at https://example.com/contract.pdf using the DeepRead skill and save the resulting markdown text to my local documents folder.'

Tips & Limitations

  • Tip: Always use the webhook integration for large batches to avoid hanging your terminal while waiting for processing to complete.
  • Tip: Take advantage of the 2,000 pages per month free tier to prototype your data extraction pipelines without incurring costs.
  • Limitation: Accuracy depends on the clarity of the source image; ensure scans are high-resolution for optimal results.
  • Limitation: The HIL flag functionality is essential for high-stakes environments where 100% precision is required; do not rely on raw output for critical legal filings without manual validation of flagged fields.

Metadata

Author@uday390
Stars946
Views0
Updated2026-02-13
View Author Profile
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-uday390-deepread-ocr": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#ocr#automation#pdf-processing#data-extraction#ai-document-analysis
Safety Score: 4/5

Flags: file-read, external-api