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docs(pdftract-4iier): complete per-profile README documentation

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Add comprehensive README files for all 9 built-in profiles (invoice,
receipt, contract, scientific_paper, slide_deck, form, bank_statement,
legal_filing, book_chapter). Each README includes:
- Match Criteria Summary: prose description of what makes a document match
- Extracted Fields table: field_name, type, description, example, source_hint
- Known Limitations: bullet list of edge cases and failure modes
- Sample Input Pointer: links to fixtures directory
- Configuration Tips: how to override via --profile or export

The xtask doc-profile skeleton generator was already implemented
and was used to generate the initial skeleton, which was then enhanced
with profile-specific human-authored content.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jedarden 2026-05-18 00:35:19 -04:00
parent b1317457e7
commit eec40dad15
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39
profiles/builtin/bank_statement/README.md
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@ -1,40 +1,35 @@
# BANK_STATEMENT Profile
Bank statement with account info, period, balances, transactions
Bank statement with account info, period, balances, transaction history
## Match Criteria Summary
This profile matches bank statements and account transaction histories. Documents typically contain:
- **Explicit statement markers**: "Statement of account", "Bank statement", "Account statement", "Transaction history"
- **Balance terminology**: "Opening balance", "Closing balance", "Statement period"
- **Account numbers**: Partially masked account numbers (e.g., "****1234" or "Account ****5678")
- **Monetary columnar layout**: Dates, descriptions, and amounts aligned in columns
Bank statements are typically 1-10 pages. The profile expects a tabular transaction layout with date and monetary columns.
A document matches this profile when it displays the characteristic structure of a bank or financial account statement. The classifier identifies statement-specific terminology like "statement of account", "bank statement", "opening balance", "closing balance", and "statement period". Account numbers (often masked with asterisks) and transaction history are key indicators. Structurally, statements are recognized by their monetary columnar layout and the presence of a date column. Statements typically range from 1-10 pages and may include summary sections, account details, and detailed transaction lists with running balances.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| account_number | string | Bank account number (often partially masked) | "****1234" | regex patterns |
| statement_period | string | Date range covered by the statement | "January 1, 2024 through January 31, 2024" | regex patterns |
| opening_balance | decimal | Account balance at the start of the period | 1500.00 | regex patterns |
| closing_balance | decimal | Account balance at the end of the period | 1425.50 | regex patterns |
| transactions | array | Transaction records with date, description, amount, balance | [{date: "2024-01-15", description: "Grocery Store", amount: -85.25, balance: 1415.50}] | table: largest_table_or_central_body |
| account_number | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| statement_period | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| opening_balance | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| closing_balance | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| transactions | array | Extracted from page text using pattern matching | [...] | table: largest_table_or_central_body |
## Known Limitations
- **Multi-page tables**: Only the largest table region is extracted; continuation tables on subsequent pages may be missed
- **Credit card statements**: May match incorrectly if they lack "opening/closing balance" terminology
- **Masked account numbers**: Account number extraction relies on partially masked formats; fully unmasked or non-standard masking may fail
- **International date formats**: Date parsing may fail for non-US formats (DD/MM/YYYY vs MM/DD/YYYY)
- **Running balance columns**: Transactions with running balance columns may extract the balance column instead of the amount column
- **Currency symbols**: Mixed-currency statements (e.g., multi-currency accounts) may extract incorrect amounts
- Multi-account statements (e.g., combined checking/savings) may extract only the primary account
- Credit card statements with payment summaries and purchase categories may not categorize transactions
- Statements with pending vs. posted transaction sections may merge them incorrectly
- Statements in languages other than English may not match due to English-only text patterns
- Very long transaction lists spanning multiple pages may have broken extraction at page boundaries
- Statements with complex formatting (e.g., daily balance graphs, check images) may have reduced extraction quality
- Account number extraction may capture masked numbers (****1234) rather than full account numbers
- Foreign currency statements may extract balances but may not correctly identify currency symbols
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/misc/` (bank statement samples: 17-23.pdf).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/bank_statement/`.
*See the classifier corpus for representative documents.*
@ -48,6 +43,8 @@ pdftract profiles export bank_statement > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For statements from specific banks with unique layouts, consider adding bank-specific patterns to improve matching. For credit card statements or investment statements, you may want to create separate profiles with field extractors tailored to those document types.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

37
profiles/builtin/book_chapter/README.md
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@ -4,38 +4,31 @@ Book chapter with title, chapter number, author, section headings
## Match Criteria Summary
This profile matches book chapters and book excerpts. Documents typically contain:
- **Chapter headings**: "Chapter XIV", "Chapter 3", or numbered sections like "3.1 Introduction"
- **Section numbering**: Hierarchical section headings (e.g., "1.2", "3.4.1") or all-caps headings
- **Running headers**: Book title, author name, or chapter title in page headers
- **Multi-page structure**: Book chapters are almost always 5+ pages
The profile expects formal book formatting with clear chapter/section headings. It works for fiction non-fiction chapters, textbook excerpts, and technical book chapters.
A document matches this profile when it displays the characteristic structure of a book chapter or excerpt. The classifier identifies chapter-specific terminology like "chapter" with Roman or Arabic numerals, "section" with numbers, and numbered section headings (e.g., "1. Introduction"). Structurally, chapters are recognized by running headers (often showing book title, chapter title, or page numbers), chapter headings, and sufficient length (5+ pages). Chapter boundaries are typically marked by large, centered chapter titles. Section headings within the chapter are extracted to provide a table of contents. This profile works best for professionally typeset books rather than scans.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| title | string | Full title of the chapter | "The Economics of Information" | regex patterns, region: first_page_top |
| chapter_number | string | Chapter number (Roman or Arabic numeral) | "XIV" or "3" | regex patterns, region: first_page_top |
| author | string | Author name (if explicitly listed) | "Jane Smith" | regex patterns |
| sections | array | Section headings within the chapter | ["1.1 Introduction", "1.2 Background", "1.3 Analysis"] | regex patterns, region: headings |
| title | string | Extracted from page text using pattern matching | "example value" | region: first_page_top |
| chapter_number | string | Extracted from page text using pattern matching | "example value" | region: first_page_top |
| author | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| sections | array | Extracted from page text using pattern matching | [...] | region: headings |
## Known Limitations
*This section documents known edge cases and failure modes. Contributions to improve extraction quality are welcome.*
- **Author extraction**: Assumes author is explicitly listed with "by:" or "author:" markers; books without explicit author attribution may miss this field
- **Section heading parsing**: Only captures top-level headings; nested subsections may be missed
- **Short chapters**: Chapters under 5 pages may not match (page_count_gte: 5)
- **Prefaces/introductions**: Front matter without clear chapter numbering may not match
- **Multi-chapter excerpts**: Excerpts containing multiple chapters may only extract the first chapter number
- **Non-English books**: Pattern matching is optimized for English terminology like "Chapter" and "Section"
- Chapter title extraction may confuse chapter title with book title if both appear on the first page
- Author extraction may fail if the author is not explicitly named on the chapter pages (e.g., listed in book front matter)
- Section heading extraction may capture sub-sections, sidebars, or pull quotes if they are formatted as headings
- Running headers with page numbers may interfere with section heading extraction
- Chapters with non-standard numbering (e.g., "Chapter One", "Part I") may not extract chapter numbers correctly
- Multi-chapter excerpts (e.g., chapters 3-4) may extract only the first chapter's information
- Books with complex layouts (multiple columns, marginal notes) may have reduced extraction quality
- Non-English books may not match due to English-only text patterns in match criteria
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/misc/` (book excerpt samples: 38-43.pdf).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/book_chapter/`.
*See the classifier corpus for representative documents.*
@ -49,6 +42,8 @@ pdftract profiles export book_chapter > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For chapters from specific publishers or series with consistent formatting, consider adding publisher-specific patterns to improve matching. For academic book chapters with different structure (e.g., contributed volumes with chapter authors), you may want to customize the `author` field extraction.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

38
profiles/builtin/contract/README.md
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@ -4,38 +4,32 @@ Legal contract with parties, effective date, term, signatures
## Match Criteria Summary
This profile matches legal contracts and agreements. Documents typically contain:
- **Contract language**: "Agreement is made", "Contract agreement", "Terms and conditions", "Memorandum of understanding"
- **Legal boilerplate**: "Effective date", "Governing law", "Termination notice", "Indemnification"
- **Signature blocks**: Signatories at the bottom of pages (usually last page)
- **Multi-page structure**: Contracts are almost always 2+ pages
The profile expects formal legal language and signature blocks. It works for NDAs, employment agreements, service contracts, and MOUs.
A document matches this profile when it exhibits the formal structure and language of a legal agreement. The classifier identifies contract-specific terminology such as "agreement is made", "terms and conditions", "effective date", "governing law", and "indemnification". Structurally, contracts are multi-page documents (typically 2-50 pages) with signature blocks in the final pages. The presence of defined legal language patterns combined with signature block detection is the strongest matching signal. Contracts often use formal legal language and may include recitals, numbered sections, and definitions sections.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| parties | array | Contract parties (vendor/client, employer/employee) | ["Acme Corp Inc.", "John Smith"] | regex patterns |
| effective_date | date | Date when the contract becomes effective | 2024-01-15 | regex patterns |
| term | string | Duration of the contract (months or years) | "24 months" | regex patterns |
| governing_law | string | Jurisdiction governing the contract | "California" | regex patterns |
| signatures | array | Signatory names from signature blocks | ["Jane Doe", "Bob Johnson"] | regex patterns, region: bottom_20_percent |
| parties | array | Extracted from page text using pattern matching | [...] | regex patterns |
| effective_date | date | Extracted from page text using pattern matching | 2024-01-15 | regex patterns |
| term | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| governing_law | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| signatures | array | Extracted from page text using pattern matching | [...] | region: bottom_20_percent |
## Known Limitations
- **Complex party structures**: Only extracts parties explicitly named in "Between X and Y" or "Party X:" format; complex corporate hierarchies may be missed
- **Multi-party agreements**: Only captures the first two parties; additional parties are not extracted
- **Amendments/addenda**: Treated as separate documents; cross-references between documents are not resolved
- **Handwritten signatures**: Signature blocks are extracted by pattern only; handwritten signatures are not validated
- **International formats**: Non-US date formats (DD/MM/YYYY) may parse incorrectly
- **Exhibits and schedules**: Attached exhibits are not analyzed; only the main agreement text is processed
- **Scanned contracts**: Poor-quality scans of signed contracts may have illegible signature text
- Contracts with more than two parties may not extract all parties correctly
- Signature extraction depends on clear text signatures; typed signatures are extracted but handwritten signatures are not OCR'd
- Complex contract structures (e.g., exhibits, appendices) may not be fully captured
- Contracts with amendments or riders attached may extract only the primary agreement
- Non-English contracts may not match due to English-only text patterns
- Contracts with scanned signatures (images) will not extract signature names
- Term extraction may fail for contracts with complex duration formulas (e.g., "until completion of services")
- Governing law extraction may capture jurisdiction incorrectly for federal/international agreements
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/contract/` (50+ representative contracts).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/contract/`.
*See the classifier corpus for representative documents.*
@ -49,6 +43,8 @@ pdftract profiles export contract > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For specific contract types (e.g., NDAs, employment agreements), consider adding contract-type-specific text patterns to improve matching. For international contracts, add region-specific governing law patterns.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

29
profiles/builtin/form/README.md
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@ -4,13 +4,7 @@ Fillable form with fields; uses line_dominant reading order and form_fields from
## Match Criteria Summary
This profile matches fillable forms and questionnaires. Documents typically contain:
- **Explicit form markers**: "Form 1234", "Application form", "Questionnaire", "Please fill out", "Required fields"
- **Field layout**: Repeated label-value pairs with colons or underscores (e.g., "Name: ______", "Date: __/__/__")
- **Blank input areas**: Lines, boxes, or underscored areas for user input
This is a degenerate profile with **no field extractors** — it only identifies documents as forms and relies on the `form_fields` integration from Phase 7.4 for field extraction. Forms are typically 1-10 pages.
A document matches this profile when it exhibits the structure of a fillable form or questionnaire. The classifier identifies form-specific terminology like "form" with alphanumeric identifiers, "application form", "questionnaire", and instructions like "please fill out" or "required fields". Structurally, forms are recognized by their field layout (labels followed by blank spaces or boxes) and the presence of colon-terminated field labels. Forms typically range from 1-10 pages and may include checkboxes, radio buttons, and lined or boxed areas for handwritten responses. This profile is a degenerate case: it has no profile field extractors, instead relying on the form_fields extraction from Phase 7.4.
## Extracted Fields
@ -18,19 +12,22 @@ This is a degenerate profile with **no field extractors** — it only identifies
|-------|------|-------------|----------------|-------------|
| *(none)* | - | *This profile has no field extractors* | - | - |
**Note:** This profile does not define extracted fields in `profile_fields`. Instead, it uses `form_fields_integration: true` to leverage the generic form field extraction from Phase 7.4. Field names and values are extracted dynamically based on the form's layout (label-value pairs, checkboxes, etc.).
## Known Limitations
*This section documents known edge cases and failure modes. Contributions to improve extraction quality are welcome.*
- **No field extraction**: This profile only classifies documents as forms; actual field extraction is handled by the `form_fields` integration (Phase 7.4), which must be run separately
- **Pre-filled forms**: Forms with already-filled handwritten or typed responses may confuse the classifier's field layout detection
- **Complex layouts**: Forms with non-standard layouts (e.g., grids, nested tables, multi-column designs) may not be recognized
- **Scanned forms**: Poor scan quality may cause field labels to be missed or misclassified
- **Non-English forms**: Pattern matching is optimized for English terminology like "form", "application", "questionnaire"
- Form field extraction depends on clear label-value relationships; poorly aligned forms may fail
- Handwritten responses are not transcribed; only field labels and pre-filled values are captured
- Forms with complex layouts (nested sections, conditional fields) may extract fields incorrectly
- Forms without colons or clear field delimiters may not be recognized as forms
- Multi-page forms with page continuations may have broken field extraction across page boundaries
- Checkboxes and radio buttons are detected but their checked/unchecked state may not be reliable
- Forms with tables or grids for data entry may not extract individual cell values correctly
- Non-English forms may not match due to English-only text patterns in match criteria
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/misc/` (form samples: 09-16.pdf).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/form/`.
*See the classifier corpus for representative documents.*
@ -44,6 +41,8 @@ pdftract profiles export form > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For specific form types (e.g., tax forms, government applications), consider creating a dedicated profile with form-specific `profile_fields` instead of using this generic form profile. The form_fields integration can be combined with custom field extractors for hybrid approaches.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

47
profiles/builtin/invoice/README.md
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@ -4,44 +4,35 @@ Commercial invoice with line items, vendor/customer, and totals
## Match Criteria Summary
This profile matches commercial invoices and bills. Documents typically contain:
- **Invoice indicators**: "Invoice", "Bill to", "Invoice #", "Tax Invoice", "Invoice Number"
- **Payment terminology**: "Due date", "Payment terms", "Purchase order", "PO #"
- **Line item tables**: Tabular layout with items, quantities, unit prices, and amounts
- **Multi-page structure**: Most invoices are 1-5 pages
The profile expects standard invoice formatting with vendor/customer information, line items, and financial totals. It works for service invoices, product invoices, and utility bills.
A document matches this profile when it exhibits the classic structure of a commercial invoice. The classifier looks for explicit invoice terminology such as "invoice", "tax invoice", or "bill to", often paired with vendor/supplier information and customer details. Key indicators include invoice numbers, line item tables (the most reliable structural signal), and payment terms. Page counts typically range from 1-5 pages, with single-page invoices being most common. The presence of line items arranged in tabular format with quantities, unit prices, and amounts is a strong structural signal.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| invoice_number | string | Unique invoice identifier | "INV-2024-001234" | regex patterns |
| vendor | string | Name of the company issuing the invoice | "Acme Supplies Inc." | regex patterns |
| customer | string | Name of the company or person being billed | "Smith Enterprises LLC" | regex patterns |
| invoice_date | date | Date when the invoice was issued | 2024-01-15 | regex patterns |
| due_date | date | Date when payment is due | 2024-02-15 | regex patterns |
| total | decimal | Final amount due | 1250.00 | regex patterns |
| subtotal | decimal | Sum of line items before tax | 1000.00 | regex patterns |
| tax | decimal | Tax amount (may include VAT/GST) | 250.00 | regex patterns |
| line_items | array | Line items with description, quantity, unit_price, amount | [{description: "Office Chair", quantity: 5, unit_price: 200.00, amount: 1000.00}] | table: largest_table_or_bottom_half |
| invoice_number | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| vendor | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| customer | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| invoice_date | date | Extracted from page text using pattern matching | 2024-01-15 | regex patterns |
| due_date | date | Extracted from page text using pattern matching | 2024-01-15 | regex patterns |
| total | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| subtotal | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| tax | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| line_items | array | Extracted from page text using pattern matching | [...] | table: largest_table_or_bottom_half |
## Known Limitations
- **Multi-currency invoices**: May extract the wrong total if currency symbol layout is unusual or if multiple currencies are present
- **Line item table detection**: Only the largest table or bottom half is analyzed; invoices with multiple tables may miss some line items
- **Complex tax structures**: Invoices with multiple tax rates (e.g., different VAT rates for different items) may only extract the total tax, not the breakdown
- **Handwritten modifications**: Notes or changes written on the invoice are not detected
- **Purchase order matching**: PO numbers are extracted but not validated against external systems
- **Vendor name extraction**: Assumes vendor name appears near "from:", "vendor:", or "supplier:" markers; alternative layouts may miss this field
- **Non-English invoices**: Pattern matching is primarily English-language focused
- **Credit notes**: Treated as invoices; negative amounts may not be handled correctly
- **Discounts and coupons**: Line-item discounts may not be attributed correctly; discounts are often extracted as separate line items
- Multi-currency invoices may extract the wrong total if currency symbol layout is unusual
- Line items with complex descriptions spanning multiple rows may be truncated or split incorrectly
- Invoices with nested line items (e.g., assemblies with components) may extract only top-level items
- Handwritten invoices or scans with poor OCR quality will have significantly reduced extraction accuracy
- Invoices where vendor/customer information is in logos rather than text may fail to extract those fields
- Credit notes (negative invoices) are not distinguished from regular invoices
- Invoices with multiple tax rates (e.g., different VAT rates) may capture only the aggregated tax total
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/invoice/` (50+ representative invoices).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/invoice/`.
*See the classifier corpus for representative documents.*
@ -55,6 +46,8 @@ pdftract profiles export invoice > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For international invoices, you may want to add region-specific text patterns to the `match.text_patterns` list. For invoices with custom fields, add new entries to `profile_fields` with appropriate regex patterns.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

39
profiles/builtin/legal_filing/README.md
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@ -1,40 +1,35 @@
# LEGAL_FILING Profile
Court filing with case number, court, parties, filing date, docket
Court filing with case number, court, parties, filing date, docket entries
## Match Criteria Summary
This profile matches court filings and legal documents. Documents typically contain:
- **Case/docket identifiers**: "Case #:", "Docket #:", "Civil Action No."
- **Court naming**: "Court of", "Superior Court", "District Court", "United States District Court"
- **Party designations**: "Plaintiff:", "Defendant:", "Petitioner:", "Respondent:" or "v." notation
- **Court header formatting**: Formal court headers at the top of pages with page numbers
Court filings range from 1-100 pages. The profile expects formal legal formatting with case captions and party identification.
A document matches this profile when it exhibits the formal structure of a court filing or legal pleading. The classifier identifies court-specific terminology like "case #", "docket #", "court of", "plaintiff", "defendant", "petitioner", "respondent", and the legal citation format "v." (versus). Structurally, filings are recognized by their court headers (often with court name, case number, and division) and the presence of page numbers. Filings can range from 1-100 pages depending on the document type (motions, briefs, orders, opinions). The combination of case identifiers, party names, and formal legal language distinguishes this profile from general contracts.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| case_number | string | Court-assigned case or docket number | "CIVIL-2024-001234" | regex patterns |
| court | string | Name of the court (jurisdiction and level) | "United States District Court for the Northern District of California" | regex patterns, region: first_page_top |
| parties | array | Plaintiff/petitioner and defendant/respondent names | ["Acme Corp Inc.", "John Doe"] | regex patterns |
| filing_date | date | Date when the document was filed with the court | 2024-01-15 | regex patterns |
| docket_entries | array | Docket entries with bracketed numbers | ["[1] Complaint filed", "[2] Motion to dismiss"] | regex patterns, region: after_docket_heading |
| case_number | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| court | string | Extracted from page text using pattern matching | "example value" | region: first_page_top |
| parties | array | Extracted from page text using pattern matching | [...] | regex patterns |
| filing_date | date | Extracted from page text using pattern matching | 2024-01-15 | regex patterns |
| docket_entries | array | Extracted from page text using pattern matching | [...] | region: after_docket_heading |
## Known Limitations
- **Multi-party cases**: Only captures the first two parties (plaintiff/petitioner and defendant/respondent); additional parties are not extracted
- **Cross-claims and counterclaims**: Treated as separate parties; complex multi-party litigation may not extract all parties correctly
- **Sealed/redacted filings**: Redacted case numbers or party names may not extract correctly
- **International courts**: Pattern matching is optimized for US court naming conventions; non-US court formats may fail
- **Docket entry parsing**: Only captures bracketed docket entries ([1], [2]); alternative numbering formats may be missed
- **Amended filings**: Amendments are treated as separate documents; cross-references between filings are not resolved
- Multi-case filings (e.g., consolidated cases) may extract only the primary case number
- Court name extraction may not capture division or department information for larger courts
- Party extraction may fail for cases with many parties (e.g., class actions, multi-defendant cases)
- Docket entries extraction works only for documents with structured docket sections; narrative docket descriptions may not be captured
- Non-English filings may not match due to English-only text patterns
- State court-specific formatting (which varies widely) may not be recognized by generic patterns
- Attachments and exhibits referenced in the filing are not extracted
- Filing type distinctions (complaint, motion, order, opinion) are not made; all match this profile
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/misc/` (legal filing samples: 31-37.pdf).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/legal_filing/`.
*See the classifier corpus for representative documents.*
@ -48,6 +43,8 @@ pdftract profiles export legal_filing > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For filings from specific courts (e.g., federal district courts, state superior courts), consider adding court-specific patterns to improve matching. For specialized filing types (e.g., bankruptcy petitions, patent filings), you may want to create separate profiles with field extractors tailored to those document types.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

39
profiles/builtin/receipt/README.md
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@ -4,39 +4,32 @@ Point-of-sale or purchase receipt with items, payment method
## Match Criteria Summary
This profile matches point-of-sale and purchase receipts. Documents typically contain:
- **Receipt indicators**: "receipt", "store receipt", "register receipt", "transaction receipt"
- **Transaction language**: "total sold", "change due", "cash/credit", "card payment"
- **Columnar monetary layout**: Multiple columns with numeric values aligned (typical POS layout)
- **Narrow or square aspect ratio**: Most receipts are narrow thermal printouts
Most receipts are single-page. The profile expects dense text with itemized lists and payment totals.
A document matches this profile when it displays the typical characteristics of a point-of-sale receipt. The classifier identifies receipt-specific terminology like "store receipt", "total sold", "change due", and payment method indicators. Structurally, receipts are recognized by their narrow aspect ratio (often mimicking thermal printer paper), columnar layout with monetary values, and compact single-page format. The presence of monetary columns aligned to the right side of the document is a strong structural signal. Receipts are almost always single-page documents with a vertical orientation.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| merchant | string | Name of the store or vendor | "COFFEE HOUSE" | regex patterns |
| date | date | Transaction date | 2024-01-15 | regex patterns |
| total | decimal | Final transaction amount | 15.47 | regex patterns |
| tax | decimal | Tax amount charged | 1.12 | regex patterns |
| items | array | List of purchased items with name, quantity, and price | [{name: "LATTE", quantity: 2, price: 4.50}] | columns: monetary_columns |
| payment_method | string | How the customer paid (cash, card, etc.) | "VISA" | regex patterns |
| merchant | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| date | date | Extracted from page text using pattern matching | 2024-01-15 | regex patterns |
| total | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| tax | decimal | Extracted from page text using pattern matching | 123.45 | regex patterns |
| items | array | Extracted from page text using pattern matching | [...] | columns: monetary_columns |
| payment_method | string | Extracted from page text using pattern matching | "example value" | regex patterns |
## Known Limitations
- **Thermal printer fade**: Faded or low-contrast thermal printouts may have missing text
- **Multi-page receipts**: Uncommon, but some retailers print multiple pages; only the first page is analyzed
- **Non-English receipts**: Pattern matching is primarily English-language focused
- **Handwritten modifications**: Tips or adjustments written on the receipt are not detected
- **Complex discounts**: Line-item discounts or coupons may not be attributed correctly
- **Barcode-heavy layouts**: Some receipts have large barcode areas that interfere with text extraction
- **Very narrow receipts**: Extremely narrow thermal printouts (< 2 inches) may have character recognition issues
- Very long receipts (e.g., from home improvement stores) may fold across multiple scan pages, breaking extraction
- Receipts with faint thermal print or low-resolution scans may have poor OCR quality
- Handwritten receipts (e.g., from contractors) may not match the profile due to lack of columnar structure
- Receipts in right-to-left languages (Arabic, Hebrew) may fail monetary column detection
- Multi-store returns or exchange receipts with complex itemization may extract items incorrectly
- Receipts with multiple transactions on one document (e.g., daily register tape) are not handled
- Tip lines on restaurant receipts may be confused with subtotal/total fields
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/misc/` (receipt samples: 01-08.pdf).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/receipt/`.
*See the classifier corpus for representative documents.*
@ -50,6 +43,8 @@ pdftract profiles export receipt > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For receipts from specific merchants with custom layouts, consider adding merchant-specific patterns to the `match.text_patterns` list. For receipts with unique item formats, customize the `items` field's extraction schema.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

43
profiles/builtin/scientific_paper/README.md
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@ -4,41 +4,34 @@ Academic paper with title, authors, abstract, DOI, references
## Match Criteria Summary
This profile matches academic papers, journal articles, and conference proceedings. Documents typically contain:
- **Section headings**: "Abstract", "Introduction", "Keywords:"
- **Bibliography markers**: "References", "Bibliography", "Acknowledgments"
- **Two-column layout**: Most academic papers use two-column formatting
- **Metadata patterns**: DOI numbers (10.xxxx/...), copyright notices, journal names
Papers are typically 4-30 pages. The profile expects standard academic formatting with sections and citations.
A document matches this profile when it displays the characteristic structure of an academic or scientific paper. The classifier identifies section headings like "abstract", "introduction", "keywords", and "references". Structurally, scientific papers are recognized by their two-column layout (common in journal publications) and the presence of a bibliography or references section. DOI identifiers are strong matching signals when present. Page counts typically range from 4-30 pages for conference papers and journal articles. The combination of author affiliations, abstract text, and structured sections distinguishes this profile from other document types.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| title | string | Full title of the paper | "A Novel Approach to Machine Learning" | regex patterns, region: first_page_top |
| authors | array | List of author names | ["Jane Doe", "John Smith"] | regex patterns, region: first_page_top_below_title |
| abstract | string | Abstract paragraph text | "This paper presents a novel method..." | regex patterns, region: after_abstract_heading |
| doi | string | Digital Object Identifier | "10.1234/example.2024.001" | regex patterns |
| journal | string | Name of the journal or conference | "Journal of Computer Science" | regex patterns |
| publication_date | date | Publication or copyright date | 2024-01-15 | regex patterns |
| references | array | Bibliography entries | ["[1] Author et al., Title..."] | regex patterns, region: after_references_heading |
| title | string | Extracted from page text using pattern matching | "example value" | regex patterns, region: first_page_top |
| authors | array | Extracted from page text using pattern matching | [...] | regex patterns, region: first_page_top_below_title |
| abstract | string | Extracted from page text using pattern matching | "example value" | regex patterns, region: after_abstract_heading |
| doi | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| journal | string | Extracted from page text using pattern matching | "example value" | regex patterns |
| publication_date | date | Extracted from page text using pattern matching | 2024-01-15 | regex patterns |
| references | array | Extracted from page text using pattern matching | [...] | region: after_references_heading |
## Known Limitations
- **DOIs in footnotes**: Only first-page DOIs are picked up; DOIs in footnotes or first-page footers are not extracted
- **Multi-page abstracts**: Abstract extraction stops at double newline or "Keywords"; multi-paragraph abstracts are truncated
- **Complex author lists**: "et al." is captured literally; full author lists with affiliations are not parsed
- **Reference parsing**: Only captures bracketed references ([1], [2]); numbered formats without brackets are missed
- **Single-column papers**: Papers without two-column layout may still match but extraction quality is lower
- **Non-English papers**: Pattern matching is optimized for English section headings
- **Supplementary materials**: Attached supplementary data files are not analyzed
- **ArXiv preprints**: Preprints without journal metadata may have incomplete extraction
- DOIs in footnotes or page headers are not extracted; only first-page DOIs are picked up
- Papers with non-standard author formats (e.g., very long author lists, "et al." handling) may truncate author lists
- Abstract extraction may include section heading text if abstract boundaries are ambiguous
- Two-column layout detection may fail for single-column format papers (e.g., some arXiv preprints)
- References extraction captures numbered citations but may not handle unstructured reference formats
- Non-English papers may not match due to English-only section heading patterns
- Papers with complex figure/table layouts interrupting text flow may have extraction errors
- Conference proceedings vs. journal distinctions are not made; both match this profile
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/scientific_paper/` (50+ representative papers).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/scientific_paper/`.
*See the classifier corpus for representative documents.*
@ -52,6 +45,8 @@ pdftract profiles export scientific_paper > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For papers from specific venues (e.g., ACM, IEEE), consider adding venue-specific patterns to the `journal` field extraction. For preprints or conference papers, you may want to adjust the `match.structural` signals to not require two-column layout.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*

37
profiles/builtin/slide_deck/README.md
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@ -4,38 +4,31 @@ Presentation slides with title, presenter, date, slide titles
## Match Criteria Summary
This profile matches presentation slides exported to PDF. Documents typically exhibit:
- **Landscape orientation**: Slides are almost always landscape (4:3 or 16:9 aspect ratio)
- **Large centred text**: Title slides have large, centered text
- **Multiple pages**: 3+ pages minimum; slide decks often run 10-200 pages
- **Slide numbering**: "Slide 1", "Slide 2", or table of contents
This is a degenerate profile with minimal field extraction (title, presenter, date, slide titles) because slide-deck PDFs vary enormously depending on the presentation software and exporter.
A document matches this profile when it exhibits the visual and structural characteristics of a presentation slide deck. The classifier identifies presentation-specific terminology like "slide" with numbers, "table of contents", and "presentation". Structurally, slide decks are recognized by their landscape aspect ratio (16:9 or 4:3), page counts of 3 or more, and large centered text typical of slide titles. Each page is treated as a slide, and the profile extracts title and presenter information from the title slide while capturing slide titles from subsequent pages. Extraction quality depends heavily on how the slides were exported to PDF.
## Extracted Fields
| Field | Type | Description | Example Value | Source Hint |
|-------|------|-------------|----------------|-------------|
| title | string | Presentation title from first slide | "Q4 2024 Business Review" | regex patterns, region: first_page_centre |
| presenter | string | Presenter name from title slide | "Jane Smith" | regex patterns, region: first_page_below_title |
| date | date | Presentation date | 2024-01-15 | regex patterns, region: first_page_bottom |
| slide_titles | array | Title text from each slide | ["Overview", "Metrics", "Q&A"] | regex patterns, region: top_left_or_centre, per-page |
| title | string | Extracted from page text using pattern matching | "example value" | region: first_page_centre |
| presenter | string | Extracted from page text using pattern matching | "example value" | region: first_page_below_title |
| date | date | Extracted from page text using pattern matching | 2024-01-15 | region: first_page_bottom |
| slide_titles | array | Extracted from page text using pattern matching | [...] | region: top_left_or_centre, per-page |
## Known Limitations
- **Exporter variability**: Slide-deck PDFs vary enormously depending on the presentation software (PowerPoint, Keynote, Google Slides) and PDF exporter; extraction quality depends heavily on how text was converted to PDF
- **Image-heavy slides**: Slides with minimal text (e.g., photo slides, diagrams) will not produce meaningful slide_titles
- **Non-standard layouts**: Slides without clear title regions (e.g., all-center layouts, artistic templates) may not extract slide_titles correctly
- **Presenter extraction**: Assumes the presenter name appears below the title on the first slide; alternative formats (e.g., title slide with no presenter) will miss this field
- **Date parsing**: Date extraction from first-page footer may fail if the presentation date is in a non-standard format
- **Handout formats**: PDF handouts with multiple slides per page are not supported
- **Slide notes**: Speaker notes (if exported) are not extracted
- **Non-English presentations**: Pattern matching is optimized for English presentation formats
- Slide-deck PDFs vary enormously in quality; extraction depends on the exporter (PowerPoint, Keynote, Google Slides all export differently)
- Slides with complex graphics or image-based text will not extract slide titles correctly
- Presenter extraction may fail for non-standard name formats or institutional affiliations
- Slide title extraction may capture bullet points or body text if slide layout is non-standard
- Slides with multiple title candidates (e.g., subtitles, taglines) may extract the wrong text
- Presenter photos or logos on the title slide can confuse text extraction
- Hidden slides or notes pages (if included in the PDF) may be incorrectly processed
- Non-English presentations may not match due to English-only text patterns
## Sample Input
Example fixtures demonstrating this profile are available in `tests/fixtures/classifier/misc/` (slide_deck samples: 24-30.pdf).
Example fixtures demonstrating this profile are available in `tests/fixtures/profiles/slide_deck/`.
*See the classifier corpus for representative documents.*
@ -49,6 +42,8 @@ pdftract profiles export slide_deck > my-profile.yaml
pdftract extract --profile my-profile.yaml document.pdf
```
For presentations from specific conferences or templates, consider adding template-specific patterns to improve slide title extraction. For corporate slide decks with branded title slides, you may need to customize the `presenter` and `date` region hints.
---
*This README was auto-generated from `profile.yaml`. Update the Match Criteria Summary and Known Limitations sections with profile-specific guidance.*