Document Parsing

Document Parsing

1. Overview

Provides Claude with extraction rules for converting receipt images and PDF invoices into structured line-item data. This skill covers:

• Danish grocery receipts (thermal paper with many line items)
• Telecom invoices (PDF with service line items)
• Utility bills (PDF with consumption data)
• Restaurant receipts
• Online order confirmations

It also provides a workflow for looking up and applying vendor-specific invoice parsers from invoice-knowledge/.

2. Vendor Detection & Parser Lookup

Parser Lookup Workflow

When processing a receipt or invoice, follow this decision tree:

1. Detect vendor (see Vendor Detection below)
2. Normalize vendor name to vendor-id (lowercase, no spaces)
3. Check: does invoice-knowledge/{vendor-id}/PARSER.md exist?
   ├── YES: Load PARSER.md → use vendor-specific extraction rules
   │         Set confidence boost: +0.1 (parser-assisted extraction)
   └── NO:  Fall back to general rules (Section 3 and 4 below)
            Set confidence: 0.5-0.7 (general extraction)
            After processing: suggest /smartspender:receipt learn

Vendor Detection Signals

Detect the vendor using these signals in priority order:

Vendor ID Normalization

For vendors not in this table: normalize to lowercase, replace spaces with hyphens, remove A/S and other suffixes.

Parser Application

When a PARSER.md is found, apply it as follows:

1. Load Parser: Read invoice-knowledge/{vendor-id}/PARSER.md into context
2. Extract Metadata: Use the parser's extraction rules instead of generic rules
3. Extract Line Items: Use the parser's line items table to map invoice lines to categories
4. Apply Special Handling: Follow any special handling rules in the parser
5. Check Learned Corrections: Apply corrections from the Learned Corrections table
6. Set Confidence: Parser-assisted extractions get baseline 0.8 (vs. 0.5-0.7 for general)

Fallback Behavior

When no vendor-specific parser exists:

1. Use general extraction rules from sections 3-4 below
2. Set extraction confidence to 0.5-0.7 (lower than parser-assisted)
3. After processing completes and the user has verified/corrected the extraction, suggest:

Tip: Du kan koere /smartspender:receipt learn for at gemme
udtraeksregler for {vendor}, saa fremtidige fakturaer
fra denne leverandoer bliver mere praecise.

Only suggest this for PDF invoices (not grocery receipts or simple card slips).

Available Parsers

3. Grocery Receipt Extraction

Supported Receipt Types

Structure Recognition

Danish grocery receipts follow a consistent layout:

[Store name and address]
[Date and time]
[Cashier / register info]
---
[Item lines]
...
---
[Subtotal]
[Discounts]
[Total]
[Payment method]
[VAT summary]

Item Line Patterns

Each item line typically follows one of these formats:

Danish Keywords

Discount Handling

Discounts on Danish receipts appear in several formats:

Total Validation

After extracting all items, compare the sum of line totals against the receipt total:

• Variance <= 2%: Accept without comment
• Variance 2-5%: Accept but note: "Bemaerk: Summen af varerne afviger lidt fra totalen (pant, afrunding, eller manglende linje)."
• Variance > 5%: Flag for review: "Summen af varerne ({sum} kr) matcher ikke kvitteringens total ({total} kr). Tjek venligst om alle linjer er korrekt aflaest."

4. PDF Invoice Extraction

Structure Recognition

Danish PDF invoices typically contain:

[Company logo and name]
[Invoice number and date]
[Customer info]
[Billing period]
---
[Service line items]
---
[Subtotal before VAT]
[Moms (25%)]
[Total including moms]
[Payment info / due date]

Key Fields to Extract

Danish Number Formats in PDFs

Danish invoices use comma as decimal separator and period as thousands separator:

• 1.234,56 means 1234.56
• 299,00 means 299.00

Convert all amounts to standard decimal format (period as decimal separator) for storage.

5. Product Subcategory Taxonomy

Product-level subcategories for receipt line items. These are more granular than the merchant-level subcategories in skills/categorization/SKILL.md.

Dagligvarer (Groceries) Subcategories

Bolig (Housing) Subcategories

Abonnementer (Subscriptions) Subcategories

6. Confidence Scoring

Rate the quality of each extraction:

Store the confidence in match_confidence on the receipts.csv row (this is extraction confidence, separate from the transaction match confidence in skills/transaction-matching/SKILL.md).

7. Edge Cases

Blurry or Low-Quality Receipts

If the receipt image is too blurry to extract reliably:

1. Extract whatever is readable (merchant, date, total at minimum)
2. Set confidence to 0.3-0.4
3. Set item_count to 0 if no items are readable
4. Inform user: "Kvitteringen er svaer at aflaese. Jeg kunne kun finde butik, dato og total. Vil du tilfoeje varelinjer manuelt?"

Multiple Receipts in One Image

If the image contains more than one receipt:

1. Process only the first/most prominent receipt
2. Inform user: "Billedet ser ud til at indeholde flere kvitteringer. Jeg har behandlet den foerste. Del venligst de andre enkeltvis."

Non-Danish Receipts

If the receipt is not in Danish:

1. Still attempt extraction (most receipt formats are similar)
2. Use the receipt's currency if not DKK
3. Note in file_reference: "non-danish"
4. Categories still use Danish names

Receipts Without Line Items

Some receipts only show a total (e.g., card terminal slips):

1. Create the receipts.csv row with item_count = 0
2. Skip writing to receipt-items files (don't create an empty monthly file)
3. Inform user: "Denne kvittering har kun en total — ingen varelinjer fundet."

Negative Items (Returns)

Items marked with RETUR or negative amounts:

1. Store with negative total_price
2. Category matches the original item if identifiable
3. Discount field stays 0 (the negative price is the return, not a discount)

8. Examples

Example 1: Grocery Receipt Extraction

Input: Photo of Foetex receipt

Extracted metadata:

merchant: Foetex
date: 2026-01-28
total: 347.50
currency: DKK
source: upload

Extracted items:

1. Minimælk 1L          qty:2  unit:12.95  total:25.90  cat:Dagligvarer  sub:Mejeriprodukter
2. Hakket oksekød 500g  qty:1  unit:45.00  total:45.00  cat:Dagligvarer  sub:Kød
3. Øko bananer          qty:1  unit:22.95  total:22.95  cat:Dagligvarer  sub:Frugt og grønt
4. Coca-Cola 1.5L       qty:2  unit:22.00  total:44.00  cat:Dagligvarer  sub:Drikkevarer
5. Rødvin Chianti       qty:1  unit:79.95  total:79.95  cat:Dagligvarer  sub:Alkohol
6. Tandbørstehoveder    qty:1  unit:89.95  total:89.95  cat:Sundhed      sub:Personlig pleje
7. Rugbrød              qty:1  unit:24.95  total:24.95  cat:Dagligvarer  sub:Brød og bagværk
8. Pose 2 kr            qty:1  unit:2.00   total:2.00   cat:Andet        sub:Andet

Sum: 334.70 vs total 347.50 — variance 3.7% — accept with note about pant/rounding.

Example 2: TDC Invoice Extraction

Input: PDF invoice from TDC

Extracted metadata:

merchant: TDC
date: 2026-01-15
total: 299.00
currency: DKK
source: upload

Extracted items:

1. Mobilabonnement Frihed+  qty:1  unit:199.00  total:199.00  cat:Abonnementer  sub:Mobilabonnement
2. Ekstra data 10GB         qty:1  unit:49.00   total:49.00   cat:Abonnementer  sub:Mobilabonnement
3. Forsikring Mobil+        qty:1  unit:51.00   total:51.00   cat:Abonnementer  sub:Forsikring

Sum: 299.00 vs total 299.00 — exact match.

Example 3: Known Vendor (TDC) with Parser

Input: PDF invoice uploaded, filename "TDC_faktura_jan2026.pdf"

Vendor detection:

1. Filename contains "TDC" → vendor candidate: TDC
2. Check invoice-knowledge/tdc/PARSER.md → exists
3. Load TDC parser

Parser-assisted extraction:

• date: Look for "Fakturadato" → 15-01-2026
• total: Look for "I alt inkl. moms" → 299,00
• Line items: Match "Mobilabonnement" → Abonnementer > Mobilabonnement

Confidence: 0.95 (all fields extracted cleanly with parser)

Example 4: Unknown Vendor (Norlys, No Parser Yet)

Input: PDF invoice from Norlys

Vendor detection:

1. Invoice header shows "Norlys" → vendor candidate: Norlys
2. Normalize to vendor-id: norlys
3. Check invoice-knowledge/norlys/PARSER.md → does not exist

Fallback extraction:

• Use general PDF invoice rules
• Extract merchant, date, total, line items using generic patterns

Confidence: 0.6 (general extraction, no vendor-specific rules)

Post-processing: After user verifies, suggest: "Tip: Du kan koere /smartspender:receipt learn for at gemme udtraeksregler for Norlys..."

Related Skills

• See skills/data-schemas/SKILL.md for the CSV file structure
• See skills/transaction-matching/SKILL.md for how extracted receipts are matched to bank transactions
• See skills/categorization/SKILL.md for merchant-level categories (receipt subcategories are more granular)
• See skills/email-receipt-scanning/SKILL.md for email-specific vendor detection signals

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