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Tencent SkillHub · AI
DeepRead OCR
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.
skill openclawclawhub Free
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.
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Documentation
DeepRead - Production OCR API
DeepRead is an 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.
What This Skill Does
DeepRead is a production-grade document processing API that gives you high-accuracy structured data output in minutes with human review flagging so manual review is limited to the flagged exceptions Core Features: Text Extraction: Convert PDFs and images to clean markdown Structured Data: Extract JSON fields with confidence scores HIL Interface: Built-in Human-in-the-Loop review — uncertain fields are flagged (hil_flag) so only exceptions need manual review Multi-Pass Processing: Multiple validation passes for maximum accuracy Multi-Model Consensus: Cross-validation between models for reliability Free Tier: 2,000 pages/month (no credit card required)
1. Get Your API Key
Sign up and create an API key: # Visit the dashboard https://www.deepread.tech/dashboard # Or use this direct link https://www.deepread.tech/dashboard/?utm_source=clawdhub Save your API key: export DEEPREAD_API_KEY="sk_live_your_key_here"
2. Clawdbot Configuration (Optional)
Add to your clawdbot.config.json5: { skills: { entries: { "deepread": { enabled: true // API key is read from DEEPREAD_API_KEY environment variable // Do NOT hardcode your API key here } } } }
3. Process Your First Document
Option A: With Webhook (Recommended) # Upload PDF with webhook notification curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@document.pdf" \ -F "webhook_url=https://your-app.com/webhooks/deepread" # Returns immediately { "id": "550e8400-e29b-41d4-a716-446655440000", "status": "queued" } # Your webhook receives results when processing completes (2-5 minutes) Option B: Poll for Results # Upload PDF without webhook curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@document.pdf" # Returns immediately { "id": "550e8400-e29b-41d4-a716-446655440000", "status": "queued" } # Poll until completed curl https://api.deepread.tech/v1/jobs/550e8400-e29b-41d4-a716-446655440000 \ -H "X-API-Key: $DEEPREAD_API_KEY"
Basic OCR (Text Only)
Extract text as clean markdown: # With webhook (recommended) curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" \ -F "webhook_url=https://your-app.com/webhook" # OR poll for completion curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" # Then poll curl https://api.deepread.tech/v1/jobs/JOB_ID \ -H "X-API-Key: $DEEPREAD_API_KEY" Response when completed: { "id": "550e8400-...", "status": "completed", "result": { "text": "# INVOICE\n\n**Vendor:** Acme Corp\n**Total:** $1,250.00..." } }
Structured Data Extraction
Extract specific fields with confidence scoring: curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" \ -F 'schema={ "type": "object", "properties": { "vendor": { "type": "string", "description": "Vendor company name" }, "total": { "type": "number", "description": "Total invoice amount" }, "invoice_date": { "type": "string", "description": "Invoice date in MM/DD/YYYY format" } } }' Response includes confidence flags: { "status": "completed", "result": { "text": "# INVOICE\n\n**Vendor:** Acme Corp...", "data": { "vendor": { "value": "Acme Corp", "hil_flag": false, "found_on_page": 1 }, "total": { "value": 1250.00, "hil_flag": false, "found_on_page": 1 }, "invoice_date": { "value": "2024-10-??", "hil_flag": true, "reason": "Date partially obscured", "found_on_page": 1 } }, "metadata": { "fields_requiring_review": 1, "total_fields": 3, "review_percentage": 33.3 } } }
Complex Schemas (Nested Data)
Extract arrays and nested objects: curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" \ -F 'schema={ "type": "object", "properties": { "vendor": {"type": "string"}, "total": {"type": "number"}, "line_items": { "type": "array", "items": { "type": "object", "properties": { "description": {"type": "string"}, "quantity": {"type": "number"}, "price": {"type": "number"} } } } } }'
Page-by-Page Breakdown
Get per-page OCR results with quality flags: curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@contract.pdf" \ -F "include_pages=true" Response: { "result": { "text": "Combined text from all pages...", "pages": [ { "page_number": 1, "text": "# Contract Agreement\n\n...", "hil_flag": false }, { "page_number": 2, "text": "Terms and C??diti??s...", "hil_flag": true, "reason": "Multiple unrecognized characters" } ], "metadata": { "pages_requiring_review": 1, "total_pages": 2 } } }
✅ Use DeepRead For:
Invoice Processing: Extract vendor, totals, line items Receipt OCR: Parse merchant, items, totals Contract Analysis: Extract parties, dates, terms Form Digitization: Convert paper forms to structured data Document Workflows: Any process requiring OCR + data extraction Quality-Critical Apps: When you need to know which extractions are uncertain
❌ Don't Use For:
Real-time Processing: Processing takes 2-5 minutes (async workflow) Batch >2,000 pages/month: Upgrade to PRO or SCALE tier
Multi-Pass Pipeline
PDF → Convert → Rotate Correction → OCR → Multi-Model Validation → Extract → Done The pipeline automatically handles: Document rotation and orientation correction Multi-pass validation for accuracy Cross-model consensus for reliability Field-level confidence scoring
Human-in-the-Loop (HIL) Interface
DeepRead includes a built-in Human-in-the-Loop (HIL) review system. The AI compares extracted text to the original image and sets hil_flag on each field: hil_flag: false = Clear, confident extraction → Auto-process hil_flag: true = Uncertain extraction → Routed to human review How HIL works: Fields extracted with high confidence are auto-approved Uncertain fields are flagged with hil_flag: true and a reason Only flagged fields need human review (typically 5-10% of total fields) Review flagged fields in DeepRead Preview (preview.deepread.tech) — a dedicated HIL review interface where reviewers can see the original document side-by-side with extracted data, correct flagged fields, and approve results Or integrate with your own review queue using the hil_flag data in the API response AI flags extractions when: Text is handwritten, blurry, or low quality Multiple possible interpretations exist Characters are partially visible or unclear Field not found in document This is multimodal AI determination, not rule-based.
1. Blueprints (Optimized Schemas)
Create reusable, optimized schemas for specific document types: # List your blueprints curl https://api.deepread.tech/v1/blueprints \ -H "X-API-Key: $DEEPREAD_API_KEY" # Use blueprint instead of inline schema curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" \ -F "blueprint_id=660e8400-e29b-41d4-a716-446655440001" Benefits: 20-30% accuracy improvement over baseline schemas Reusable across similar documents Versioned with rollback support How to create blueprints: # Create a blueprint from training data curl -X POST https://api.deepread.tech/v1/optimize \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "name": "utility_invoice", "description": "Optimized for utility invoices", "document_type": "invoice", "initial_schema": { "type": "object", "properties": { "vendor": {"type": "string", "description": "Vendor name"}, "total": {"type": "number", "description": "Total amount"} } }, "training_documents": ["doc1.pdf", "doc2.pdf", "doc3.pdf"], "ground_truth_data": [ {"vendor": "Acme Power", "total": 125.50}, {"vendor": "City Electric", "total": 89.25} ], "target_accuracy": 95.0, "max_iterations": 5 }' # Returns: {"job_id": "...", "blueprint_id": "...", "status": "pending"} # Check optimization status curl https://api.deepread.tech/v1/blueprints/jobs/JOB_ID \ -H "X-API-Key: $DEEPREAD_API_KEY" # Use blueprint (once completed) curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" \ -F "blueprint_id=BLUEPRINT_ID"
2. Webhooks (Recommended for Production)
Get notified when processing completes instead of polling: curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@invoice.pdf" \ -F "webhook_url=https://your-app.com/webhooks/deepread" Your webhook receives this payload when processing completes: { "job_id": "550e8400-...", "status": "completed", "created_at": "2025-01-27T10:00:00Z", "completed_at": "2025-01-27T10:02:30Z", "result": { "text": "...", "data": {...} }, "preview_url": "https://preview.deepread.tech/abc1234" } Benefits: No polling required Instant notification when done Lower latency Better for production workflows
3. Preview (HIL Review Interface)
DeepRead Preview (preview.deepread.tech) is the built-in Human-in-the-Loop review interface. Reviewers can view the original document alongside extracted data, correct flagged fields, and approve results. Preview URLs can also be shared without authentication: # Request preview URL curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@document.pdf" \ -F "include_images=true" # Get preview URL in response { "result": { "text": "...", "data": {...} }, "preview_url": "https://preview.deepread.tech/Xy9aB12" } Public Preview Endpoint: # No authentication required curl https://api.deepread.tech/v1/preview/Xy9aB12
Free Tier (No Credit Card)
2,000 pages/month 10 requests/minute Full feature access (OCR + structured extraction + blueprints)
Paid Plans
PRO: 50,000 pages/month, 100 requests/minute @ $99/mo SCALE: Custom volume pricing (contact sales) Upgrade: https://www.deepread.tech/dashboard/billing?utm_source=clawdhub
Rate Limit Headers
Every response includes quota information: X-RateLimit-Limit: 2000 X-RateLimit-Remaining: 1847 X-RateLimit-Used: 153 X-RateLimit-Reset: 1730419200
1. Use Webhooks for Production
✅ Recommended: Webhook notifications curl -X POST https://api.deepread.tech/v1/process \ -H "X-API-Key: $DEEPREAD_API_KEY" \ -F "file=@document.pdf" \ -F "webhook_url=https://your-app.com/webhook" Only use polling if: Testing/development Cannot expose a webhook endpoint Need synchronous response
2. Schema Design
✅ Good: Descriptive field descriptions { "vendor": { "type": "string", "description": "Vendor company name. Usually in header or top-left of invoice." } } ❌ Bad: No description { "vendor": {"type": "string"} }
3. Polling Strategy (If Needed)
Only if you can't use webhooks, poll every 5-10 seconds: import time import requests def wait_for_result(job_id, api_key): while True: response = requests.get( f"https://api.deepread.tech/v1/jobs/{job_id}", headers={"X-API-Key": api_key} ) result = response.json() if result["status"] == "completed": return result["result"] elif result["status"] == "failed": raise Exception(f"Job failed: {result.get('error')}") time.sleep(5)
4. Handling Quality Flags
Separate confident fields from uncertain ones: def process_extraction(data): confident = {} needs_review = [] for field, field_data in data.items(): if field_data["hil_flag"]: needs_review.append({ "field": field, "value": field_data["value"], "reason": field_data.get("reason") }) else: confident[field] = field_data["value"] # Auto-process confident fields save_to_database(confident) # Send uncertain fields to review queue if needs_review: send_to_review_queue(needs_review)
Error: quota_exceeded
{"detail": "Monthly page quota exceeded"} Solution: Upgrade to PRO or wait until next billing cycle.
Error: invalid_schema
{"detail": "Schema must be valid JSON Schema"} Solution: Ensure schema is valid JSON and includes type and properties.
Error: file_too_large
{"detail": "File size exceeds 50MB limit"} Solution: Compress PDF or split into smaller files.
Job Status: failed
{"status": "failed", "error": "PDF could not be processed"} Common causes: Corrupted PDF file Password-protected PDF Unsupported PDF version Image quality too low for OCR
Invoice Schema
{ "type": "object", "properties": { "invoice_number": { "type": "string", "description": "Unique invoice ID" }, "invoice_date": { "type": "string", "description": "Invoice date in MM/DD/YYYY format" }, "vendor": { "type": "string", "description": "Vendor company name" }, "total": { "type": "number", "description": "Total amount due including tax" }, "line_items": { "type": "array", "items": { "type": "object", "properties": { "description": {"type": "string"}, "quantity": {"type": "number"}, "price": {"type": "number"} } } } } }
Receipt Schema
{ "type": "object", "properties": { "merchant": { "type": "string", "description": "Store or merchant name" }, "date": { "type": "string", "description": "Transaction date" }, "total": { "type": "number", "description": "Total amount paid" }, "items": { "type": "array", "items": { "type": "object", "properties": { "name": {"type": "string"}, "price": {"type": "number"} } } } } }
Contract Schema
{ "type": "object", "properties": { "parties": { "type": "array", "items": {"type": "string"}, "description": "Names of all parties in the contract" }, "effective_date": { "type": "string", "description": "Contract start date" }, "term_length": { "type": "string", "description": "Duration of contract" }, "termination_clause": { "type": "string", "description": "Conditions for termination" } } }
Support & Resources
GitHub: https://github.com/deepread-tech Issues: https://github.com/deepread-tech/deep-read-service/issues Email: hello@deepread.tech
Important Notes
Processing Time: 2-5 minutes (async, not real-time) Async Workflow: Use webhooks (recommended) or polling Rate Limits: 10 req/min on free tier File Size Limit: 50MB per file Supported Formats: PDF, JPG, JPEG, PNG Ready to start? Get your free API key at https://www.deepread.tech/dashboard/?utm_source=clawdhub
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
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- package.json