Intelligent Data Extraction System from PDF Invoices with CUG Line Output - Scalable Solution
Budget: €250 – €750 EUR
- THE PROBLEM
I need to automate data extraction from supplier invoices in PDF and image formats. My management system requires EXACT data matching to function properly.
Main technical challenge:
The software requires perfect string matching: [Code] + [Description] + [Supplier]
Even minor variations (spaces, capitalization) cause recognition errors and require manual intervention.
- REQUIRED OUTPUT - CUG LINE
The system must produce a structured record (CUG Line) for EVERY LINE of each invoice containing:
1. supplier_name
2. supplier_vat_number
3. customer_name
4. customer_vat_number
5. product_code
6. product_description
7. batch_number (if present)
8. expiry_date (if present)
9. quantity_purchased
10. unit_of_measure
11. unit_price
12. total_price
13. discount (if present)
14. tax_code
CRITICAL: Each field must be extracted consistently and deterministically. The same product must ALWAYS produce the same product_description.
- TECHNICAL REQUIREMENTS
Input:
Native PDFs
Scanned PDFs
Photos of invoices (often taken with phones, poor lighting, angled, blurry)
Photo preprocessing required:
Photos need automatic enhancement before extraction:
Perspective correction (deskew, dewarp)
Quality enhancement (denoise, sharpen, contrast adjustment)
Lighting correction (remove shadows, uniform brightness)
Rotation/cropping (auto-detect document boundaries)
Resolution optimization for OCR
Main challenges:
Each supplier uses different structures
Batch/expiry dates in variable positions (same line, next line, 2 lines after, or absent)
Need deterministic output for same input
CUG Line format must be complete for each product line
Photos often have poor quality requiring preprocessing
- WHAT I'M LOOKING FOR
A solution that:
Preprocesses photos automatically for optimal readability
Extracts ALL required fields for the CUG Line
Guarantees consistency in outputs (same product = same description always)
Handles optional fields (batch/expiry might not exist)
Learns and improves over time
Produces structured output ready for API/webhook
- BUDGET
< $800 USD for initial MVP development
Open to reasonable monthly operational costs for cloud services/APIs.
- WHAT TO INCLUDE IN YOUR PROPOSAL
1. IMAGE PREPROCESSING APPROACH
Explain:
How you'll handle poor quality photos (blur, shadows, angles)
Which image enhancement techniques you'll implement
How you'll ensure preprocessing doesn't lose important text
Automatic vs manual quality checks
2. DETAILED EXTRACTION APPROACH
Explain EXACTLY:
How you'll extract ALL 14 fields of the CUG Line
How you'll handle batch/expiry in variable positions
How you'll guarantee deterministic output
Output format (JSON, CSV, API call?)
3. SPECIFIC TOOLS & TECHNOLOGY
Clearly indicate:
Image preprocessing libraries/methods
OCR/extraction technology stack and WHY
How you handle missing/optional fields
How you normalize data for consistency
Estimated monthly operational costs
4. HANDLING SPECIAL CASES
Explain how you manage:
Very poor quality photos (almost unreadable)
Multi-page invoices
Product lines spanning multiple text lines
Batch/expiry in different formats
Products without codes (description only)
Discounts in various formats
5. SCALABILITY PLAN
How the system starts simple and grows
How accuracy improves over time
How it adapts to new formats
Performance with increasing volumes
- KEY QUESTIONS TO ADDRESS
In your proposal, answer:
Photo quality: What's your approach when photos are severely degraded?
Preprocessing impact: How do you ensure image enhancement doesn't distort text?
Data completeness: How do you ensure extraction of ALL 14 fields with different formats?
Batch and expiry dates: What's your strategy for finding them in non-standard positions?
Normalization: How do you ensure consistent output for similar inputs?
Learning mechanism: How does the system improve accuracy over time?
- SUCCESS METRICS
The system will be evaluated on:
Photo enhancement: % of photos successfully made readable
Completeness: % of fields correctly extracted
Consistency: same product → always same CUG Line
Batch/expiry accuracy: % correct when present
Missing field handling: proper signaling of absent fields
- EXPECTED DELIVERABLES
Complete preprocessing → extraction → CUG Line system
Automatic photo enhancement module
API/webhook for data delivery
Logging system to track accuracy
Performance monitoring capability
CUG Line format documentation
Source code with documentation
- IMPORTANT NOTES
System must handle very poor quality photos automatically
CUG Line must be complete even if some fields are empty
Deterministic: same document → always same CUG Line
Scalable: must handle growing volumes
Learning capability: improve over time especially on batch/expiry extraction
- SELECTION CRITERIA
I will evaluate proposals based on:
Image preprocessing strategy and experience
Technical clarity and feasibility
Understanding of the CUG Line requirement
Strategy for difficult fields (batch/expiry)
Normalization approach
Realistic accuracy estimates over time
- TO APPLY
Start your proposal with:
Your image preprocessing approach for poor quality photos
Your proposed technology stack and why it's optimal
Specific strategy for variable batch/expiry extraction
Normalization method for consistent outputs
Realistic accuracy timeline (1 month vs 6 months)
Monthly operational cost estimate
Generic proposals will be ignored. I want to understand EXACTLY how you'll build this system, especially the photo preprocessing pipeline.
Note: The ideal solution should be pragmatic and incremental, not perfect from day one. The system must be able to evolve without complete rewrites.
I need to automate data extraction from supplier invoices in PDF and image formats. My management system requires EXACT data matching to function properly.
Main technical challenge:
The software requires perfect string matching: [Code] + [Description] + [Supplier]
Even minor variations (spaces, capitalization) cause recognition errors and require manual intervention.
- REQUIRED OUTPUT - CUG LINE
The system must produce a structured record (CUG Line) for EVERY LINE of each invoice containing:
1. supplier_name
2. supplier_vat_number
3. customer_name
4. customer_vat_number
5. product_code
6. product_description
7. batch_number (if present)
8. expiry_date (if present)
9. quantity_purchased
10. unit_of_measure
11. unit_price
12. total_price
13. discount (if present)
14. tax_code
CRITICAL: Each field must be extracted consistently and deterministically. The same product must ALWAYS produce the same product_description.
- TECHNICAL REQUIREMENTS
Input:
Native PDFs
Scanned PDFs
Photos of invoices (often taken with phones, poor lighting, angled, blurry)
Photo preprocessing required:
Photos need automatic enhancement before extraction:
Perspective correction (deskew, dewarp)
Quality enhancement (denoise, sharpen, contrast adjustment)
Lighting correction (remove shadows, uniform brightness)
Rotation/cropping (auto-detect document boundaries)
Resolution optimization for OCR
Main challenges:
Each supplier uses different structures
Batch/expiry dates in variable positions (same line, next line, 2 lines after, or absent)
Need deterministic output for same input
CUG Line format must be complete for each product line
Photos often have poor quality requiring preprocessing
- WHAT I'M LOOKING FOR
A solution that:
Preprocesses photos automatically for optimal readability
Extracts ALL required fields for the CUG Line
Guarantees consistency in outputs (same product = same description always)
Handles optional fields (batch/expiry might not exist)
Learns and improves over time
Produces structured output ready for API/webhook
- BUDGET
< $800 USD for initial MVP development
Open to reasonable monthly operational costs for cloud services/APIs.
- WHAT TO INCLUDE IN YOUR PROPOSAL
1. IMAGE PREPROCESSING APPROACH
Explain:
How you'll handle poor quality photos (blur, shadows, angles)
Which image enhancement techniques you'll implement
How you'll ensure preprocessing doesn't lose important text
Automatic vs manual quality checks
2. DETAILED EXTRACTION APPROACH
Explain EXACTLY:
How you'll extract ALL 14 fields of the CUG Line
How you'll handle batch/expiry in variable positions
How you'll guarantee deterministic output
Output format (JSON, CSV, API call?)
3. SPECIFIC TOOLS & TECHNOLOGY
Clearly indicate:
Image preprocessing libraries/methods
OCR/extraction technology stack and WHY
How you handle missing/optional fields
How you normalize data for consistency
Estimated monthly operational costs
4. HANDLING SPECIAL CASES
Explain how you manage:
Very poor quality photos (almost unreadable)
Multi-page invoices
Product lines spanning multiple text lines
Batch/expiry in different formats
Products without codes (description only)
Discounts in various formats
5. SCALABILITY PLAN
How the system starts simple and grows
How accuracy improves over time
How it adapts to new formats
Performance with increasing volumes
- KEY QUESTIONS TO ADDRESS
In your proposal, answer:
Photo quality: What's your approach when photos are severely degraded?
Preprocessing impact: How do you ensure image enhancement doesn't distort text?
Data completeness: How do you ensure extraction of ALL 14 fields with different formats?
Batch and expiry dates: What's your strategy for finding them in non-standard positions?
Normalization: How do you ensure consistent output for similar inputs?
Learning mechanism: How does the system improve accuracy over time?
- SUCCESS METRICS
The system will be evaluated on:
Photo enhancement: % of photos successfully made readable
Completeness: % of fields correctly extracted
Consistency: same product → always same CUG Line
Batch/expiry accuracy: % correct when present
Missing field handling: proper signaling of absent fields
- EXPECTED DELIVERABLES
Complete preprocessing → extraction → CUG Line system
Automatic photo enhancement module
API/webhook for data delivery
Logging system to track accuracy
Performance monitoring capability
CUG Line format documentation
Source code with documentation
- IMPORTANT NOTES
System must handle very poor quality photos automatically
CUG Line must be complete even if some fields are empty
Deterministic: same document → always same CUG Line
Scalable: must handle growing volumes
Learning capability: improve over time especially on batch/expiry extraction
- SELECTION CRITERIA
I will evaluate proposals based on:
Image preprocessing strategy and experience
Technical clarity and feasibility
Understanding of the CUG Line requirement
Strategy for difficult fields (batch/expiry)
Normalization approach
Realistic accuracy estimates over time
- TO APPLY
Start your proposal with:
Your image preprocessing approach for poor quality photos
Your proposed technology stack and why it's optimal
Specific strategy for variable batch/expiry extraction
Normalization method for consistent outputs
Realistic accuracy timeline (1 month vs 6 months)
Monthly operational cost estimate
Generic proposals will be ignored. I want to understand EXACTLY how you'll build this system, especially the photo preprocessing pipeline.
Note: The ideal solution should be pragmatic and incremental, not perfect from day one. The system must be able to evolve without complete rewrites.