deepread
OCR that never fails silently. Multi-pass document processing API with intelligent quality review flags. Extract text and structured data from PDFs with AI-powered confidence scoring. Free tier - 2,000 pages/month.
Best use case
deepread is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
OCR that never fails silently. Multi-pass document processing API with intelligent quality review flags. Extract text and structured data from PDFs with AI-powered confidence scoring. Free tier - 2,000 pages/month.
Teams using deepread should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deepread/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deepread Compares
| Feature / Agent | deepread | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
OCR that never fails silently. Multi-pass document processing API with intelligent quality review flags. Extract text and structured data from PDFs with AI-powered confidence scoring. Free tier - 2,000 pages/month.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# DeepRead - Production OCR API
OCR that never fails silently. Process PDFs and extract structured data with AI-powered confidence scoring that tells you exactly which fields need human review.
## What This Skill Does
DeepRead is a production-grade document processing API that **reduces human review from 100% to ~10%** through intelligent quality assessment.
**Core Features:**
- **Text Extraction**: Convert PDFs to clean markdown
- **Structured Data**: Extract JSON fields with confidence scores
- **Quality Flags**: AI determines which fields need human verification (`hil_flag`)
- **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)
## Setup
### 1. Get Your API Key
Sign up and create an API key:
```bash
# 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:
```bash
export DEEPREAD_API_KEY="sk_live_your_key_here"
```
### 2. Clawdbot Configuration (Optional)
Add to your `clawdbot.config.json5`:
```json5
{
skills: {
entries: {
"deepread": {
enabled: true,
apiKey: "sk_live_your_key_here"
}
}
}
}
```
### 3. Process Your First Document
**Option A: With Webhook (Recommended)**
```bash
# 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**
```bash
# 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"
```
## Usage Examples
### Basic OCR (Text Only)
Extract text as clean markdown:
```bash
# 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:**
```json
{
"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:
```bash
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:**
```json
{
"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:
```bash
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:
```bash
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:**
```json
{
"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
}
}
}
```
## When to Use This Skill
### ✅ 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
## How It Works
### 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
### Quality Review (hil_flag)
AI compares extracted text to the original image and sets `hil_flag`:
- **`hil_flag: false`** = Clear, confident extraction → Auto-process
- **`hil_flag: true`** = Uncertain extraction → Human review required
**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.**
## Advanced Features
### 1. Blueprints (Optimized Schemas)
Create reusable, optimized schemas for specific document types:
```bash
# 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:**
```bash
# 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:
```bash
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:**
```json
{
"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. Public Preview URLs
Share OCR results without authentication:
```bash
# 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:**
```bash
# No authentication required
curl https://api.deepread.tech/v1/preview/Xy9aB12
```
## Rate Limits & Pricing
### 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
```
## Best Practices
### 1. Use Webhooks for Production
**✅ Recommended: Webhook notifications**
```bash
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**
```json
{
"vendor": {
"type": "string",
"description": "Vendor company name. Usually in header or top-left of invoice."
}
}
```
**❌ Bad: No description**
```json
{
"vendor": {"type": "string"}
}
```
### 3. Polling Strategy (If Needed)
Only if you can't use webhooks, poll every 5-10 seconds:
```python
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:
```python
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)
```
## Troubleshooting
### Error: `quota_exceeded`
```json
{"detail": "Monthly page quota exceeded"}
```
**Solution:** Upgrade to PRO or wait until next billing cycle.
### Error: `invalid_schema`
```json
{"detail": "Schema must be valid JSON Schema"}
```
**Solution:** Ensure schema is valid JSON and includes `type` and `properties`.
### Error: `file_too_large`
```json
{"detail": "File size exceeds 50MB limit"}
```
**Solution:** Compress PDF or split into smaller files.
### Job Status: `failed`
```json
{"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
## Example Schema Templates
### Invoice Schema
```json
{
"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
```json
{
"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
```json
{
"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=clawdhubRelated Skills
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