pdftract/docs/plan/plan.md

pdftract Implementation Plan

Version: 1.0
Status: Active
Repo: jedarden/pdftract
Last updated: 2026-05-16

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Primary Objectives

pdftract must be the most accurate, fastest, and lightest-weight PDF text extraction tool available. These are not aspirational — they are acceptance criteria. Every architectural and dependency decision is evaluated against all three in priority order.

Accuracy targets (acceptance criteria — CI-gated)

Metric	Target	Measurement
Character error rate, clean vector PDFs	< 0.5%	Against ground-truth corpus, tests/fixtures/vector/
Word error rate, clean OCR (300 DPI scans)	< 3%	Against ground-truth corpus, tests/fixtures/scanned/
Reading order correctness, multi-column	> 95%	Left column entirely before right column in all fixtures
Unicode recovery rate (no ToUnicode)	> 90%	Font fingerprint + AGL levels 2–4 on tests/fixtures/encoding/
Regression gate, real-world corpus	< 0.5% CER delta vs. golden	500-PDF private corpus on every PR
Text readability score	> 0.85	Proprietary composite of printable ratio, dict word ratio, ligature repair

Speed targets (acceptance criteria — CI-gated)

Metric	Target	Measurement
100-page vector PDF, 4-core CI	< 3 seconds	cargo bench, tests/fixtures/perf/
10-page scanned PDF (OCR path), 4-core CI	< 30 seconds	includes Tesseract
Single-page extraction latency (serve mode)	< 150 ms p99	wrk benchmark against /extract
Throughput vs. pdfminer.six (Python)	≥ 10× faster	Benchmarked on identical hardware
Throughput vs. pypdf (Python)	≥ 5× faster	Same benchmark suite

Weight targets (acceptance criteria)

Metric	Target
Binary size, default features (no OCR, no serve)	< 4 MB stripped
Binary size, --features ocr,serve	< 12 MB stripped
Default dependency count (cargo tree -d)	< 20 unique crates
Shared library dependencies (ldd)	Zero beyond libc + libm
Docker image, CLI only	< 20 MB (distroless base)
Docker image, with OCR (tesseract-ocr system pkg)	< 120 MB

Decisions that violate any target require explicit justification and a waiver comment in the relevant section below.

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Overview

pdftract is a Rust PDF text extraction library with a CLI (pdftract extract), an HTTP server mode (pdftract serve), and a PyO3 Python binding. It extracts Unicode text from PDF files — including scanned pages via OCR — and produces structured JSON, NDJSON, or plain text output. The output schema is defined in docs/research/extraction-output-schema.md and is stable at schema version 1.0.

The implementation is organized into seven phases. Phases 1–4 deliver a working vector-extraction CLI. Phase 5 adds OCR. Phase 6 adds the full API surface (PyO3, HTTP). Phase 7 adds advanced features that require the Phase 1–4 foundation.

Key architectural decisions (baked in from the start)

• File I/O: memmap2 for zero-copy random access; madvise(MADV_SEQUENTIAL) on content streams.
• Object cache: LRU with 4096-entry capacity (lru crate); object streams decompressed once and cached as Arc<[u8]>.
• Parallelism: rayon for page-level parallelism; per-page work is embarrassingly parallel after Stage 1–2 complete.
• Serialization: serde + serde_json; BufWriter wrapping io::Stdout for NDJSON streaming.
• Error model: All parse errors are recoverable and produce diagnostic entries in the errors array; no panic! in library code.
• Crate layout: pdftract-core (lib), pdftract-cli (binary), pdftract-py (PyO3, optional feature).

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Dependency Matrix

Feature flags control the binary footprint. The default build (cargo build) includes only the core extraction path. Heavy optional capabilities are behind named features.

Feature flags:

• default = ["cli"] — strips to core + CLI; no OCR, no HTTP, no Python
• ocr — adds Tesseract + Leptonica (system libraries required)
• serve — adds axum + tokio (HTTP server)
• python — adds PyO3 (maturin build)
• full-render — adds pdfium-render (large native binary; improves scanned-page rasterization)
• full = ["ocr", "serve", "python"]

Crate	Version	Feature	Purpose
memmap2	0.9	default	Memory-mapped file access
flate2	1	default	FlateDecode / zlib decompression
lzw	0.10	default	LZWDecode
ttf-parser	0.21	default	TrueType/OpenType glyph metrics and cmap lookup
owned_ttf_parser	0.21	default	Arc-safe wrapper for ttf-parser
lru	0.12	default	Object cache eviction
rayon	1	default	Page-level parallelism
serde	1	default	Serialization derive macros
serde_json	1	default	JSON output
indexmap	2	default	Ordered dictionaries (PDF dict key order matters for CMap parsing)
unicode-normalization	0.1	default	NFC normalization
encoding_rs	0.8	default	CJK encoding decoding (Shift-JIS, GB18030, Big5, EUC-KR)
phf	0.11	default	Compile-time AGL hash map (zero runtime allocation)
clap	4	cli	CLI argument parsing
thiserror	1	default	Error type derivation
log + env_logger	0.4	default	Structured logging
image	0.25	ocr	Raster image decoding and DPI-scaled rendering
tesseract	0.14	ocr	Tesseract OCR FFI bindings
leptonica-plumbing	0.4	ocr	Leptonica image preprocessing (Sauvola, deskew)
quick-xml	0.36	ocr	HOCR and XFA XML parsing
pdfium-render	0.8	full-render	High-fidelity rasterization via PDFium (large native binary — ~20 MB)
pyo3	0.21	python	Python bindings
maturin	build	python	PyO3 wheel packaging
axum	0.7	serve	HTTP serve mode
tokio	1	serve	Async runtime for axum
tower-http	0.5	serve	Request size limiting and tracing
multer	3	serve	Multipart form parsing
bytes	1	serve	Zero-copy byte sharing in HTTP path

Removed vs. first draft: jpeg-decoder dropped — DCTDecode is passthrough; SOI/EOI marker validation is a 4-byte check with no external dependency. whichlang dropped — language detection is not on the critical accuracy path; BCP-47 lang tags come from PDF /Lang attributes and StructTree /Lang, not inference.

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Phase 1: Core PDF Parser (Foundation)

Goal: Parse any PDF object, resolve xref tables, decode streams. No text extraction yet.
Complexity: Complex
Estimate: 3–4 weeks
Delivers: pdftract-core::parser module usable in unit tests.

1.1 Lexer

Tokenize the raw byte slice into PDF tokens. This is the lowest layer; all higher-level parsers call into it.

Tokens to produce:

• Boolean (true, false)
• Integer (123, -7)
• Real (3.14, -.5)
• String literals: literal strings (...) with all escape sequences (\n, \r, \t, \\, \(, \), \ddd octal, line-continuation \<newline>), and hex strings <...> (odd-length padded with trailing zero nibble)
• Name objects: /Name, with #XX hex escape expansion, NUL-byte rejection, and length limit (127 bytes per spec)
• Array delimiters: [, ]
• Dictionary delimiters: <<, >>
• Stream keyword: stream (validated against following \n or \r\n)
• End-stream keyword: endstream
• Indirect object markers: obj, endobj, R
• Comments: % to end of line (discarded)
• Whitespace: consumed between tokens (0x00, 0x09, 0x0A, 0x0C, 0x0D, 0x20)

Crates: none (hand-written; nom is an option but PDF's grammar is simple enough to avoid the dependency)

Critical tests:

• String with nested balanced parentheses: (foo (bar) baz) → foo (bar) baz
• String with octal escape at end of string: (abc\101) → abcA
• Hex string with odd length: <4> → \x40
• Name with #20 → space character
• Name with #00 → rejected (NUL in name is invalid per spec; emit diagnostic)
• Whitespace-only file → empty token stream, no panic

1.2 Object Parser

Parse the token stream into the PDF object model.

Types:

• PdfNull
• PdfBool(bool)
• PdfInt(i64)
• PdfReal(f64)
• PdfString(Vec<u8>) — raw bytes before any encoding interpretation
• PdfName(Arc<str>)
• PdfArray(Vec<PdfObject>)
• PdfDict(IndexMap<Arc<str>, PdfObject>) — preserves insertion order
• PdfRef(u32, u16) — object number, generation number
• PdfStream { dict: PdfDict, offset: u64 } — offset into mmap; data decoded lazily
• PdfIndirect { id: ObjRef, obj: Box<PdfObject> }

Key behaviors:

• Indirect object parsing: N G obj ... endobj wrapper
• Object streams (/ObjStm): decompress once, parse all embedded objects, cache them under their object numbers
• Circular reference guard: track in-resolution set per thread; emit STRUCT_CIRCULAR_REF diagnostic and return PdfNull on cycle

Crates: indexmap (dict), bytes (object stream caching)

Critical tests:

• Nested dict: << /A << /B 1 >> >> — correct inner dict
• Array of mixed types: [1 true (str) /Name null]
• Object stream: decompress, parse all N objects, verify all ObjRefs resolve
• Self-referencing object (circular): returns PdfNull with diagnostic, no stack overflow

1.3 Cross-Reference Resolution

Build the complete object → byte-offset map from the file's xref structure.

Strategies (attempted in order on failure):

1. Traditional xref table: parse from startxref offset; 20-byte fixed-width entries; handle \r\n and \n line endings; merge multi-subsection tables
2. Xref streams (PDF 1.5+): parse /W field widths; decompress body with FlateDecode; parse /Index subsections; handle type-0/1/2 entries
3. Hybrid files: merge traditional table (priority) with xref stream (/XRefStm pointer); type-2 entries from stream fill gaps not covered by traditional table
4. Forward scan fallback: sequential scan for N G obj patterns; slower but handles severely truncated or overwritten files; emit XREF_REPAIRED diagnostic

Incremental updates: When /Prev is present in a trailer, recursively load the previous xref revision; later revisions override earlier entries for the same object number. This handles incremental saves, linearized files, and comment-editing workflows.

Crates: flate2 (xref stream decompression)

Critical tests:

• PDF with /Prev chain of 3 revisions: latest value of each object number wins
• Type-2 xref entry: object resolved through /ObjStm correctly
• Hybrid file: traditional entries override stream entries for same object numbers
• File truncated after xref: forward scan finds all objects before truncation point
• startxref offset off by one (common real-world corruption): forward scan triggered, XREF_REPAIRED diagnostic emitted

1.4 Document Model

Build the in-memory document model over the xref-resolved object graph.

Structures to build:

• Document catalog from /Root: record /Pages, /Outlines, /MarkInfo, /StructTreeRoot, /AcroForm, /Names, /Metadata, /PageLabels, /OCProperties
• Page tree (/Pages subtree): flatten into a Vec<PageDict> with inherited attributes resolved (MediaBox, CropBox, BleedBox, TrimBox, ArtBox, Resources, Rotate). Inheritance walk: page dict overrides parent dict; root /Pages is the ultimate fallback.
• Resource dictionary inheritance: each page gets a fully resolved ResourceDict merging all ancestor /Resources dicts (font, XObject, ExtGState, ColorSpace, Shading, Pattern, Properties namespaces). Per-key last-write-wins at the page level.
• Encryption dictionary detection: if /Encrypt present in trailer, identify handler (/Standard vs. custom), extract /V, /R, /KeyLength, /CF//StmF//StrF entries. RC4 and AES-128/256 decryption. Password attempt: empty string first, then user-supplied. On failure: emit ENCRYPTION_UNSUPPORTED and abort.

Crates: none beyond the parser layer

Critical tests:

• Page inheriting MediaBox from grandparent /Pages node
• Page overriding /Resources /Font partially (merged, not replaced)
• PageLabels number tree: pages with roman-numeral labels followed by arabic labels
• Encrypted file with empty owner password: decrypts successfully
• Encrypted file with unknown handler: ENCRYPTION_UNSUPPORTED error, no crash

1.5 Stream Decoder

Decode stream data through its filter pipeline. Called lazily when stream content is first accessed.

Filters to implement (in priority order):

Filter	Implementation	Notes
FlateDecode	flate2::read::ZlibDecoder	Apply predictor post-inflate: TIFF predictor 2, PNG predictors 10–15 (per-row byte selects predictor for value 15)
LZWDecode	lzw crate	/EarlyChange parameter: 1 = early (default), 0 = late; same predictor support as FlateDecode
ASCII85Decode	hand-written	z shortcut, partial final group, ~> terminator, embedded whitespace ignored
ASCIIHexDecode	hand-written	Digit pairs, whitespace ignored, > terminator
RunLengthDecode	hand-written	Length byte: 0–127 = copy next N+1 bytes literally; 129–255 = repeat next byte 257-N times; 128 = EOD
DCTDecode	passthrough	Pass raw JPEG bytes to consumer; validate SOI/EOI markers; log /ColorTransform for consumer
JBIG2Decode	passthrough	Pass raw JBIG2 bytes; log global stream reference
JPXDecode	passthrough	Pass raw JPEG 2000 bytes; for OCR path, decode via image crate
CCITTFaxDecode	passthrough	Pass raw CCITT bytes; for OCR path, decode via image crate
Crypt	identity only	/Name /Identity handled; custom crypt filters emit ENCRYPTION_UNSUPPORTED

Filter pipeline: /Filter is a name or array; /DecodeParms is aligned or absent. Apply decoders in order. Mismatched lengths: apply defaults, log diagnostic.

Error recovery: zlib decompression error mid-stream: return bytes decoded so far, emit STREAM_DECODE_ERROR diagnostic. Never abort the page.

Crates: flate2, lzw, jpeg-decoder (JPEG validation only), image (JPX/CCITT raster decode for OCR path)

Critical tests:

• FlateDecode with PNG predictor 15 (per-row): all six predictor types appear in one stream, all decoded correctly
• LZWDecode with EarlyChange=0: verify against known reference output
• ASCII85 with z shortcut and odd final group
• Filter array [/ASCII85Decode /FlateDecode]: decoded in order
• FlateDecode with truncated zlib stream: partial output returned, diagnostic emitted
• DCTDecode: raw bytes passed through unchanged; SOI marker present

1.6 Error Recovery

Cross-cutting concerns for malformed files.

Strategies:

• Truncated file at EOF: forward xref scan; any endobj before truncation point is valid
• Corrupt xref entry (bad offset): attempt to parse at listed offset; if first bytes are not N G obj, skip entry with diagnostic; do not remove from xref map (other objects may be valid)
• Missing required dict key: return PdfNull, emit STRUCT_MISSING_KEY diagnostic with object number; caller must handle null gracefully
• Integer overflow in object dimensions: clamp to i32::MAX and log; do not panic
• Circular object reference: detected via per-thread resolution stack; return PdfNull with diagnostic

Critical tests:

• File where 30% of xref entries point to wrong offsets: remaining 70% extracted correctly
• Missing /MediaBox on every page: default letter size (612×792) used, diagnostic emitted per page
• Object with endobj missing: parser reads to next N G obj pattern and continues

---

Phase 2: Font and Encoding Pipeline

Goal: For any character code from a content stream, resolve a Unicode scalar value and a confidence score.
Complexity: Complex
Estimate: 3–4 weeks
Depends on: Phase 1 complete
Delivers: pdftract-core::font module

2.1 Font Type Detection

Load and classify the font from the resource dictionary.

Font types and loading strategy:

Subtype	Font Program Location	Metric Source
Type1	/FontFile in FontDescriptor	/Widths array
Type1 (Standard 14)	No font program; synthesized	Known metrics table (hardcoded)
TrueType	/FontFile2	/Widths array; hmtx for verification
Type0 (composite)	Descendant CIDFont	/DW, /W array in CIDFont dict
CIDFontType0	/FontFile3 (/CIDFontType0C)	/DW, /W
CIDFontType2	/FontFile2 or /FontFile3 (/OpenType)	/DW, /W
Type3	/CharProcs content streams	/Widths
OpenType (CFF)	/FontFile3 (/OpenType)	hhea/hmtx via ttf-parser

Font subset detection: Many embedded fonts are subsets with name prefix like ABCDEF+Helvetica. Strip the six-uppercase-letter prefix before looking up Standard 14 or glyph name tables.

Crates: ttf-parser, owned_ttf_parser

Critical tests:

• Standard 14 font (no embedding): correct metrics returned without font file
• Subset font ABCDEF+Times-Roman: stripped to Times-Roman, standard metrics used
• CIDFontType2 with /CIDToGIDMap /Identity: GID == CID for all lookups
• OpenType CFF font: metrics via ttf-parser's CFF support

2.2 Encoding Resolution

Map character codes → Unicode. Four-level fallback chain with unicode_source tag on each result.

Level 1: ToUnicode CMap

Parse the /ToUnicode stream as a CMap program. CMap syntax to implement:

• beginbfchar / endbfchar: <srcCode> <dstHex> pairs; <dstHex> may be a UTF-16BE multi-codepoint sequence for ligature expansion
• beginbfrange / endbfrange: <lo> <hi> <dst> (contiguous single-codepoint range) or <lo> <hi> [<d0> <d1> ...] (explicit array for non-contiguous targets)
• usecmap directive: inherit from named CMap (e.g., Adobe-Japan1-UCS2)
• Comment lines (%) stripped

Successful lookup: set unicode_source = "to_unicode", confidence = 1.0.
Result is U+FFFD or empty: fall through to Level 2.

Level 2: Encoding vector + AGL

Map character code → glyph name via the font's /Encoding:

• Named encodings: WinAnsiEncoding, MacRomanEncoding, MacExpertEncoding, StandardEncoding, SymbolEncoding, ZapfDingbatsEncoding — hardcoded tables
• /Differences array: sparse overlay on top of base encoding; format [n /GlyphName1 /GlyphName2 ...] (n is starting code)

Map glyph name → Unicode via Adobe Glyph List (AGL 1.4, ~4400 entries, compiled in as a static phf::Map). Also support AGLFN (friendly names).

Set unicode_source = "agl", confidence = 0.9.

Level 3: Font fingerprint cache

Hash the embedded font program (SHA-256 of the font stream bytes). Look up in a bundled database of known font checksums → per-glyph Unicode mapping tables. Initially populated with the most common 200 commercial fonts.

Set unicode_source = "fingerprint", confidence = 0.85.

Level 4: Glyph shape recognition

Render the glyph to a 32×32 grayscale bitmap using the font program. Hash the bitmap with a perceptual hash. Look up in a bundled shape→Unicode database (see Phase 2.3).

Set unicode_source = "shape_match", confidence = 0.7.

Failure: Emit U+FFFD, unicode_source = "unknown", confidence = 0.0, log GLYPH_UNMAPPED diagnostic.

Crates: ttf-parser (glyph rendering for shape hash), phf (compile-time AGL hash map)

Critical tests:

• ToUnicode with multi-codepoint bfchar (fi ligature → fi): expanded to two characters
• beginbfrange with explicit array: non-contiguous targets resolved correctly
• WinAnsiEncoding code 0x92: maps to U+2019 RIGHT SINGLE QUOTATION MARK (not U+0092)
• MacRoman code 0xD2 / 0xD3: left/right double quotation marks
• Unknown glyph name not in AGL: falls through to Level 3 or 4
• Type1 font with no /Encoding and no /ToUnicode: Level 3/4 fallback triggered

2.3 CJK Encoding

Handle multi-byte CJK character sets for Type 0 composite fonts.

Predefined CMaps to implement (or reference via bundled data):

• Identity-H / Identity-V: CID == character code (passthrough)
• UniJIS-UTF16-H, UniJIS-UTF16-V: Japanese JIS → Unicode
• UniGB-UTF16-H, UniGB-UTF16-V: GB2312 → Unicode
• UniCNS-UTF16-H, UniCNS-UTF16-V: Big5/CNS → Unicode
• UniKS-UTF16-H, UniKS-UTF16-V: KS → Unicode

Encoding decoding for raw byte sequences:

• Shift-JIS: encoding_rs::SHIFT_JIS
• GB18030: encoding_rs::GB18030
• Big5: encoding_rs::BIG5
• EUC-KR: encoding_rs::EUC_KR

Multi-byte code parsing: Type 0 font's /Encoding CMap defines the codespace ranges (begincodespacerange/endcodespacerange). Parse the CMap to determine 1- vs. 2-byte code boundaries, then tokenize the content stream byte sequence accordingly.

Crates: encoding_rs

Critical tests:

• Identity-H Type 0 font with ToUnicode: CID passthrough, Unicode from ToUnicode
• Embedded Shift-JIS ToUnicode CMap: all 6879 JIS X 0208 characters resolve correctly
• Two-byte code boundary in codespace: first byte in 0x81–0xFE range triggers two-byte read; 0x00–0x7F is single-byte
• Mixed single/double-byte codes in same TJ string: all boundaries parsed correctly

2.4 Type 3 Font Handling

Type 3 fonts define each glyph as a content stream in /CharProcs. No standard Unicode mapping exists unless /ToUnicode is provided.

Pipeline:

1. Check /ToUnicode first (same Level 1 logic as above)
2. If absent, attempt /Encoding glyph name lookup (Level 2)
3. If glyph name is non-standard (arbitrary user name), rasterize the content stream to a 32×32 bitmap and apply shape recognition (Level 4)
4. Track the content stream rendering state: Type 3 glyphs can invoke other PDF operators including form XObjects; apply the same graphics state machine as Phase 3

Metrics: Use /Widths, /FirstChar, /LastChar, /FontMatrix to compute advance widths. /FontMatrix default is [1 0 0 1 0 0] for Type 3 (glyph units == text units); apply it to convert glyph-space advance to text space.

Critical tests:

• Type 3 font with meaningful /ToUnicode: resolved correctly
• Type 3 font with arbitrary glyph names and no ToUnicode: shape recognition fallback, confidence = 0.7
• Type 3 glyph stream that invokes a form XObject: recursive processing without stack overflow
• /FontMatrix [0.001 0 0 0.001 0 0]: advances scaled to 1/1000 of text units (matches Type 1)

---

Phase 3: Content Stream Processing

Goal: Execute PDF content stream operators to produce a raw glyph list with positions.
Complexity: Complex
Estimate: 3–4 weeks
Depends on: Phase 2 complete
Delivers: pdftract-core::content module; raw Vec<Glyph> per page

3.1 Graphics State Machine

Maintain the full graphics state stack as the content stream is executed.

State struct fields:

ctm: Matrix3x3           -- current transformation matrix
text_matrix: Matrix3x3   -- Tm (set by Tm/Td/TD/T*)
text_line_matrix: Matrix3x3  -- Tlm (reset by Td/TD/T*)
font: Option<Arc<Font>>
font_size: f64
char_spacing: f64        -- Tc
word_spacing: f64        -- Tw
horiz_scaling: f64       -- Tz (percentage, default 100)
leading: f64             -- TL
text_rise: f64           -- Ts
text_rendering_mode: u8  -- Tr (0–7)
fill_color: Color
stroke_color: Color

Stack operators: q pushes a clone of the current state; Q pops. Stack depth limit: 64 (per spec); deeper push emits GSTATE_STACK_OVERFLOW diagnostic and discards the push (safe failure).

Text state operators:

Operator	Effect
BT	Reset text_matrix = identity, text_line_matrix = identity
ET	End text object; discard current text matrix
Tc n	char_spacing = n
Tw n	word_spacing = n
Tz n	horiz_scaling = n
TL n	leading = n
Tf name size	Load font by resource name, set font_size
Tr n	text_rendering_mode = n
Ts n	text_rise = n
Td tx ty	text_line_matrix = translate(tx, ty) * text_line_matrix; copy to text_matrix
TD tx ty	Same as Td; also leading = -ty
Tm a b c d e f	Set both matrices directly
T*	Equivalent to Td 0 -leading

CTM operators: cm a b c d e f — multiply CTM by the given matrix.

Crates: none (hand-written matrix arithmetic; 3x3 f64 matrices, no external linear algebra dependency needed)

Critical tests:

• q/Q nesting 64 levels deep: succeeds; level 65 emits diagnostic
• Td chain: verify accumulated text_line_matrix matches manual calculation
• Tm followed by Td: Td is relative to previous text_line_matrix, not Tm
• Tr 3 (invisible): glyph produced with rendering_mode = 3
• Color operators rg, RG, k, K, cs, scn: fill/stroke color tracked correctly

3.2 Text Operator Processing

Parse text-showing operators and produce Glyph structs.

Text-showing operators:

Operator	Argument	Behavior
Tj	(string)	Show string; advance text position
TJ	[...] array	Alternate strings and numeric kerning adjustments
'	(string)	T* then Tj
"	aw ac (string)	Set word_spacing=aw, char_spacing=ac, then '

Per-glyph processing:

1. Decode character code(s) from the string bytes using the current font's codespace
2. Resolve Unicode via Phase 2 font pipeline
3. Compute glyph advance width from font metrics (accounting for Tc, Tw if space glyph, Tz)
4. Compute device-space bounding box: apply text_matrix * CTM to the glyph bbox
5. Detect word boundary: if actual next-glyph x-position > expected by more than threshold → inject synthetic space
6. Advance text_matrix by advance width

Word boundary threshold (adaptive): Initial threshold = 0.25 * font_size. After processing 20 glyphs, compute the median actual inter-glyph gap and adjust the threshold to 1.5× that median. This adapts to per-document spacing norms. See docs/research/word-boundary-reconstruction.md for full formula including Tc, Tw, Tz corrections.

TJ kerning: Numeric elements in a TJ array adjust the text position by -n/1000 * font_size * Tz/100 (negative n = kern closer, positive = move apart). Large positive values (> 0.2 * font_size) produce word boundaries.

Glyph struct:

struct Glyph {
    codepoint: char,         // resolved Unicode or U+FFFD
    unicode_source: UnicodeSource,
    confidence: f32,
    bbox: [f32; 4],          // [x0, y0, x1, y1] in PDF user space (lower-left origin)
    font_name: Arc<str>,
    font_size: f32,
    rendering_mode: u8,
    fill_color: Color,
    is_word_boundary: bool,  // synthetic space injected before this glyph
}

Critical tests:

• TeX-generated PDF with no space characters: word boundaries injected at correct positions
• TJ array with large positive kerning value (word gap): space injected
• Negative TJ kern (kern tighter): no space injected
• Glyph at Tr=3: present in output with rendering_mode=3
• Font size 0 (degenerate): glyph bbox degenerates to point; no panic

3.3 Resource Context and Form XObject Recursion

Handle nested resource scopes introduced by form XObjects (Do operator).

ResourceStack: Each page starts with its resolved resource dictionary (from Phase 1.4). When a form XObject is invoked via Do, push a new resource scope merging the form's own /Resources with the current scope (form resources shadow parent resources). Pop on return.

Form XObject execution: Retrieve the form XObject stream, decode it, and execute it as a nested content stream. The form's /Matrix entry is applied to the CTM before execution; the form's /BBox is applied as a clipping boundary. After execution, restore the pre-form CTM.

Cycle detection: Track the set of form XObject object numbers currently in the execution stack. If the same object number appears twice, emit STRUCT_XOBJECT_CYCLE diagnostic and return without executing. Stack depth limit: 20 levels.

Critical tests:

• Form XObject with its own /Resources /Font: inner font resolved from form resources, not page resources
• Form XObject with /Matrix [2 0 0 2 0 0]: all glyph bboxes in form space scaled by 2
• Form XObject cycle (A invokes B invokes A): cycle detected at second A; diagnostic emitted; extraction continues
• Form XObject with empty content stream: no crash, no glyphs produced

3.4 Marked Content Tracking

Track BDC/BMC/EMC marked content sequences for MCID association (used in Phase 7 StructTree exploitation).

Operators:

• BMC /Tag and BDC /Tag << props >> or BDC /Tag /PropName: push tag frame with tag name and optional MCID from properties dict (/MCID key)
• EMC: pop tag frame

Output: Each Glyph carries an optional mcid: Option<u32> — the MCID of the innermost marked content sequence enclosing it, if any.

Critical tests:

• Nested BDC: innermost MCID wins for enclosed glyphs
• EMC without matching BMC (malformed): ignored; no stack underflow panic
• MCID 0: valid (zero is a legal MCID)

3.5 Inline Images

Detect and skip inline image data (BI/ID/EI operator sequence) without confusing the parser.

Parsing: BI signals start of inline image dict; consume key-value pairs until ID; then scan raw bytes for the EI terminator (two-byte sequence \nEI where the preceding byte is not a continuation of image data — the spec requires the EI to be preceded by whitespace). Extract image bytes for passthrough.

Critical tests:

• Inline image immediately followed by text operators: text operators parsed correctly after EI
• Inline image data containing the byte sequence EI in the middle: not treated as terminator (must be preceded by whitespace)

---

Phase 4: Text Assembly and Layout

Goal: Transform raw Vec<Glyph> → structured blocks in reading order.
Complexity: Complex
Estimate: 3–4 weeks
Depends on: Phase 3 complete
Delivers: Per-page Vec<Block> with Vec<Span> in reading order; plain text output mode works

4.1 Glyph → Span Merging

Group consecutive glyphs into spans. A new span begins when any of the following change:

• font_name
• font_size (delta > 0.5pt)
• rendering_mode
• fill_color (normalized to RGB; spot colors treated as distinct)
• is_word_boundary (inject a synthetic space span or embed space in current span text)

Span struct:

struct Span {
    text: String,
    bbox: [f32; 4],          // union of member glyph bboxes
    font: Arc<str>,
    size: f32,
    color: Option<CssHexColor>,
    rendering_mode: u8,
    confidence: f32,         // minimum glyph confidence
    confidence_source: ConfidenceSource,
    lang: Option<Arc<str>>,  // filled in Phase 7 normalization
    flags: EnumSet<SpanFlag>, // bold, italic, smallcaps, subscript, superscript
}

Flag detection:

• Bold: font name contains "Bold" or FontDescriptor /Flags bit 18 set or /StemV > 120
• Italic: font name contains "Italic"/"Oblique" or /ItalicAngle != 0
• Smallcaps: font name contains "SC"/"SmallCaps" or /Flags bit 3 set
• Subscript: text_rise < -0.1 * font_size
• Superscript: text_rise > 0.1 * font_size

Critical tests:

• Mixed bold/regular in one text object: span break at font change
• Word boundary between two same-font glyphs: either space appended to previous span or new space span created (implementation choice; must round-trip to correct plain text)
• Subscript with Ts -3: SuperScript flag NOT set, Subscript flag set

4.2 Line Formation

Group spans into lines by baseline proximity.

Algorithm:

1. Compute baseline y-coordinate for each span: y0 + (bbox_height * 0.2) (approximation; exact value requires font descender metrics)
2. Cluster spans with baseline within 0.5 * median_font_size of each other → same line
3. Within a line, sort spans by x0 (left-to-right for LTR scripts)
4. RTL detection: If the majority of characters in a line have Unicode bidi category R or AL (right-to-left), sort spans by x1 descending and set direction = "rtl" on the resulting line struct

Crates: None (clustering is a simple sort + gap scan)

Critical tests:

• Two-column layout: columns not merged into one line (column gap exceeds threshold)
• Superscript span at higher y than baseline text: not treated as a separate line
• Arabic text: bidi R characters detected, spans sorted right-to-left

4.3 Column Detection

Identify column boundaries in multi-column layouts.

Algorithm: Collect the x0 and x1 coordinates of all spans on the page. Compute a histogram of x0 values at 1pt resolution. Gaps wider than 0.03 * page_width with zero span coverage are column boundary candidates. Require at least 3 lines to start in each candidate column before promoting it to a confirmed column.

Apply column labels to each span. This gates the XY-cut reading order algorithm in Phase 4.5.

Critical tests:

• Three-column academic paper: three distinct columns detected
• Full-width heading above two-column body: heading spans all columns; body spans within columns
• Single-column page: no false column splits

4.4 Block Formation

Group lines into blocks (paragraphs, headings, etc.).

Heuristics (applied in order):

1. Vertical gap: gap between consecutive lines > 1.5 * line_height → new block
2. Indent change: first line x0 differs from subsequent lines by > 0.03 * column_width → paragraph indent signal; may indicate block boundary above
3. Font size change: median font size of next line differs from current block by > 1pt → new block
4. Rendering mode change: invisible (Tr=3) text separated from visible text
5. Column boundary: span in different column from previous span → mandatory block break

Block kind assignment (heuristic):

• heading: font size > 1.2× body median AND line count == 1 (or short)
• header/footer: block y0 in top/bottom 7% of page height AND appears on 3+ consecutive pages with identical or near-identical text
• paragraph: default
• figure: bbox contains only image XObjects, no text glyphs
• list: line starts with bullet/numbered pattern (regex: ^\s*[•‣◦\-\*]\s or ^\s*\d+[\.\)]\s)
• caption: small font, follows a figure block within 2 lines

Critical tests:

• Indented first line of paragraph: not split into two blocks
• Header text appearing on pages 1–10: classified header and deduplicated
• Bullet list with mixed font sizes: all items in same list block

4.5 Reading Order

Determine the reading order of blocks within the page.

Fast path (tagged PDF): If is_tagged = true, defer to Phase 7 StructTree traversal. Set reading_order_algorithm = "struct_tree".

XY-cut algorithm (untagged, rectilinear layouts):

1. Find the widest vertical whitespace gap dividing the page's text bbox into left and right halves → split into two regions
2. For each region, find the widest horizontal gap → split into top and bottom sub-regions
3. Recurse until regions contain a single column of text
4. Reading order: left region before right; top before bottom within each region

Docstrum fallback (when XY-cut produces > 10 regions with < 3 blocks each): Compute nearest-neighbor pairs between text blocks. Build a graph of adjacency edges weighted by distance and angle. Traverse the connected components in estimated reading order (sort root nodes by page position, follow edges within each component).

Set reading_order_algorithm = "xy_cut" or "docstrum" in page output.

Crates: None (graph is a simple Vec<Edge>)

Critical tests:

• Two-column academic paper: all left-column blocks before all right-column blocks
• Magazine layout with sidebar: main text flow separated from sidebar
• Single-column text: XY-cut produces single region, no spurious splits
• Rotated page (Rotate=90): coordinate system rotated before applying algorithm

4.6 Output Serialization (Plain Text Mode)

Implement --text output as a projection of the block list.

Rules:

• Blocks serialized in reading order
• Paragraphs separated by \n\n
• Page breaks: \f (form feed, 0x0C)
• Headers and footers excluded by default; --include-headers-footers flag re-enables
• Invisible text (Tr=3) excluded unless --include-invisible-text flag set
• Watermark blocks excluded (Phase 6 watermark detection)

Critical tests:

• 10-page document: 9 form-feed characters in output
• Header block: excluded from --text output by default
• Invisible text span: excluded from --text output

4.7 Text Readability Validation and Correction

This phase is a primary accuracy differentiator. Existing extractors emit raw glyph sequences regardless of whether the output text is human-readable. pdftract validates every span and repairs or discards unreadable output, ensuring extracted text can be used directly without downstream cleanup.

Readability scoring (per-span):

Signal	Weight	Threshold
Printable Unicode fraction (non-U+FFFD, non-control)	0.35	> 0.95 → good
Dictionary word coverage (English; fast trie lookup)	0.30	> 0.60 → good
Whitespace distribution (not all one word, not all spaces)	0.15	ratio in [0.05, 0.40] → good
Ligature integrity (no split ligatures: fi, fl, ffi, ffl)	0.10	0 split ligatures → good
Glyph confidence floor (from Phase 2)	0.10	min confidence > 0.6 → good

Composite score [0.0, 1.0]. Spans below readability_threshold (default 0.5, configurable) are flagged readability: "low".

Correction pipeline (applied before flagging):

1. Ligature repair: If fi, fl, ffi, ffl, ff appear as adjacent U+FFFD + glyph (Phase 2 glyph level missed the ligature but position data shows adjacency < 0.1pt gap), reconstruct the ligature string from shape-matched component glyphs.
2. Hyphenation repair: End-of-line hyphen (-\n at right edge of column) joined with start of next line's first word. Strip the hyphen; concatenate. Applies only within the same block; do not join across block boundaries.
3. Mojibake detection: If the span contains sequences characteristic of Latin-1 interpreted as UTF-8 (e.g., é for é), attempt re-decoding via encoding_rs and accept if readability score improves.
4. Soft-hyphen removal: U+00AD (soft hyphen) stripped from output text; it is a formatting hint, not content.
5. Word-break normalization: U+200B (zero-width space), U+FEFF (BOM mid-stream), U+200C/200D (non-joiner/joiner used incorrectly) stripped unless the script requires them (Arabic, Indic).

Per-page readability score: Median of span scores, weighted by span character count. Stored in page.extraction_quality.readability. If page score < 0.5 and page is Vector class, escalate to BrokenVector and re-route to assisted OCR path (Phase 5.5).

Crates: unicode-normalization (already in default deps)

Word list: Embed a minimal 20,000-word English frequency list as a compile-time phf::Set (adds ~200 KB to binary; acceptable). Non-English documents: score only on printable fraction, whitespace distribution, and glyph confidence (skip dict lookup if lang attribute indicates non-English).

Critical tests:

• Span with split ligature U+FFFD U+0069 adjacent to f: repaired to fi
• Hyphenated word spanning line break: joined correctly, hyphen stripped
• Latin-1 mojibake é → corrected to é when re-decode raises readability score
• Page readability < 0.5 on vector page: page re-classified to BrokenVector, OCR invoked
• Non-English page (Chinese): dict-word signal disabled; score driven by printable fraction + confidence
• 20,000-word phf::Set lookup: < 100 ns per word (benchmark assertion)

---

Phase 5: OCR Integration

Goal: Extract text from scanned pages and improve broken-vector pages via Tesseract.
Complexity: Complex
Estimate: 3–4 weeks
Depends on: Phase 4 complete (OCR output feeds back into Phase 4 assembly)
Delivers: Full extraction for scanned PDFs; pdftract extract --ocr flag active

5.1 Page Classification

Classify each page to select the extraction path before any expensive work.

Signals (computed in order, short-circuit when confident):

Signal	Vector	Scanned	BrokenVector
No text operators in content stream	—	Strong	—
All text Tr=3 + full-page image	—	—	Definitive
Image coverage fraction > 0.85	—	Strong	—
Character validity rate < 0.4	—	—	Strong
Character validity rate > 0.85	Strong	—	—
Character density ratio < 0.03	—	Moderate	—

PageClass output: Vector | Scanned | Hybrid | BrokenVector with confidence: f32.

Hybrid detection: Compute per-region classification: divide page into 8×8 grid cells. Cells with text operators and high validity → vector; cells with image coverage and no text → scanned. If both types present in significant fractions → Hybrid.

Critical tests:

• Pure text PDF: all pages Vector with confidence > 0.95
• Scanned single-page PDF (image only): Scanned
• PDF/A with invisible text layer over scanned image: BrokenVector
• Hybrid page with text header and scanned body: Hybrid, correct region split

5.2 Image Extraction for Raster Pages

For Scanned and Hybrid pages, produce a raster for Tesseract.

Rendering approach — two-tier:

Default (no full-render feature): Direct image compositing. Collect all image XObjects on the page, decode each (Phase 1.5 stream decoder), and composite them onto a blank canvas using each XObject's placement matrix (CTM from cm and Do operators). This path has zero additional binary cost and handles > 90% of scanned PDFs correctly (those where the scan is a single full-page image).

full-render feature: pdfium-render (wraps Chromium's PDFium). Use when the page has complex rendering geometry — multiple overlapping images, image masks, soft masks — where compositing gets the wrong result. Binary cost: ~20 MB native library (tracked against the weight target; document in PR if this feature is enabled in the default Docker image). Enable with --features full-render at compile time or set ExtractionOptions.full_render = true at runtime (feature must be compiled in).

DPI selection:

• Standard body text (font_size > 8pt equivalent): 300 DPI
• Fine print or small text: 400 DPI
• Line art / JBIG2 pages: 200 DPI (already binary; higher DPI doesn't help)

Output: Grayscale image::GrayImage for each page region needing OCR.

Crates: image (default ocr feature), pdfium-render (full-render feature only)

5.3 Image Preprocessing

Apply the preprocessing pipeline before Tesseract invocation.

Pipeline (in order):

1. Deskew: Hough line transform on binarized image; compute dominant angle; rotate by negative angle. Skip if detected angle < 0.3° (no meaningful skew).
2. Binarization: Sauvola local adaptive thresholding for physical scans; Otsu global for digital-origin scans. Detect origin via image XObject filter: DCTDecode → Sauvola; JBIG2Decode → already binary, skip.
3. Denoising: 3×3 median filter for salt-and-pepper noise. Skip for JBIG2 (already clean binary).
4. Contrast normalization: Histogram stretch to [0, 255] after binarization.
5. Border padding: Add 10px white border on all sides (Tesseract accuracy improves with padding).

Crates: leptonica-plumbing (Sauvola, deskew via pixDeskew), image (Otsu, median filter)

Critical tests:

• 2° skewed scan: deskewed to within 0.1° before OCR
• Page with uneven lighting (shadow from binding): Sauvola thresholding produces clean binary
• Already-binary JBIG2 image: binarization step skipped, no quality degradation

5.4 Tesseract Integration

Invoke Tesseract on preprocessed raster images and parse HOCR output.

Configuration:

• Language: from ExtractionOptions.ocr_language (default ["eng"])
• Page segmentation mode: PSM_AUTO (Tesseract decides)
• Output format: HOCR XML (provides per-word bounding boxes and confidence scores)
• Tesseract init: one TessBaseAPI per thread (stored in thread_local!); avoid re-initialization cost

HOCR parsing:

• Parse ocrx_word elements: extract title attribute for bbox x0 y0 x1 y1 and x_wconf NNN (confidence 0–100 → 0.0–1.0)
• Convert HOCR pixel coordinates to PDF user-space coordinates using the DPI and page geometry
• Each HOCR word → one Span with confidence_source = "ocr"

Crates: tesseract (0.14; wraps libtesseract FFI), quick-xml (HOCR parsing)

Critical tests:

• Clean black-on-white scan of Lorem Ipsum: word error rate < 2%
• Multi-language page (English and French): both language packs loaded; correct characters extracted
• Tesseract confidence < 30 on a region: confidence = 0.3 in span output
• HOCR bbox coordinates correctly converted to PDF space after DPI scaling

5.5 Assisted OCR (BrokenVector Path)

For BrokenVector pages, use vector glyph positions as hints to improve Tesseract segmentation.

Pipeline:

1. Run Phase 3 content stream processing in position-hint mode: collect glyph bboxes but discard Unicode values (treat all as U+FFFD)
2. Convert glyph bboxes to HOCR-format word hint blocks and pass to Tesseract via SetVariable("applybox_debug", "0") and Tesseract's box-file input mode
3. Tesseract uses the hint boxes to seed its segmentation, improving word boundary detection
4. Parse HOCR output as in Phase 5.4
5. If OCR confidence > 0.7 for a region: use OCR text; if OCR confidence < 0.3: re-attempt without hints

Critical tests:

• PDF/A with invisible text layer at correct positions: OCR output better than blind OCR (validate WER delta)
• PDF/A with incorrect text layer positions (misaligned): hints discarded when Tesseract confidence drops; fallback to unaided OCR

---

Phase 6: Output and API

Goal: Deliver the full output schema, PyO3 bindings, and HTTP serve mode.
Complexity: Medium
Estimate: 3–4 weeks
Depends on: Phase 5 complete
Delivers: Shippable CLI, Python package, HTTP service

6.1 JSON Output (Full Schema)

Implement the complete output schema from docs/research/extraction-output-schema.md.

Document-level fields:

• schema_version: "1.0"
• metadata: title, author, subject, keywords, creator, producer, creation_date, modification_date, page_count, pdf_version, is_tagged, is_encrypted, conformance, contains_javascript, contains_xfa, generator
• outline: recursive bookmark tree with title, destination, level
• threads: article thread chains (Phase 7 feature; empty array in Phase 6)
• attachments: from /EmbeddedFiles name tree (Phase 7; empty array in Phase 6)
• signatures: digital signature metadata (Phase 7; empty array in Phase 6)
• form_fields: AcroForm fields with values (Phase 7; empty array in Phase 6)
• links: document-scoped URI and internal destination links
• extraction_quality: aggregate across all pages
• errors: all diagnostics emitted during extraction

Page-level fields (full schema):

• page_index, page_label, width, height, rotation, page_type
• spans: full Span array per schema
• blocks: full Block array per schema
• annotations: highlights, stamps, notes, links from /Annots
• tables: parallel table structure objects for kind: table blocks (Phase 7)

Crates: serde, serde_json

Critical tests:

• Schema validator: produce output from a known-good PDF, validate against a JSON Schema definition of the output schema
• Page with no text: spans: [], blocks: [], page_type: "blank" or "figure_only"
• Error entries: each emitted diagnostic has stable code, severity, and page_index

6.2 NDJSON Streaming Mode

Implement --stream / ExtractionOptions.streaming = true.

Frame sequence:

1. Header frame: {"frame":"header","schema_version":"1.0","metadata":{...},"outline":[...],"total_pages":N}
2. Per-page frames (emitted as each page completes via rayon): {"frame":"page","page_index":N,...}
   Note: rayon may complete pages out of order; buffer completed pages and emit in page_index order with a window of 8 pages maximum.
3. Footer frame: {"frame":"footer","extraction_quality":{...},"errors":[...],"threads":[],"attachments":[],"signatures":[],"form_fields":[],"links":[]}

BufWriter: Wrap io::Stdout in BufWriter<io::Stdout> with 128 KB buffer; flush after each frame.

Critical tests:

• 100-page document in streaming mode: frame 0 is header, frames 1–100 are pages in order, frame 101 is footer
• Out-of-order page completion: pages buffered and emitted in correct index order
• Consumer reads frame-by-frame with newline delimiter: each frame is valid JSON

6.3 PyO3 Python Bindings

Build a Python extension module exposing the extraction API.

Module: pdftract (import as import pdftract)

API surface:

# Synchronous extraction
result: dict = pdftract.extract(path: str, **options) -> dict
text: str = pdftract.extract_text(path: str, **options) -> str

# Streaming (returns an iterator of page dicts)
pages: Iterator[dict] = pdftract.extract_stream(path: str, **options)

# Options (keyword arguments mapped to ExtractionOptions):
# ocr=False, ocr_language=["eng"], include_invisible=False,
# extract_forms=False, extract_attachments=False, readability_threshold=0.5

# Exceptions
class PdftractError(Exception): ...       # extraction failed
class EncryptionError(PdftractError): ... # encrypted, no password

Python GIL handling: Release the GIL during extraction (py.allow_threads(|| ...)) so Python threads can continue while a page is being processed.

Build: maturin build --features python produces a .whl for the current platform. CI cross-compiles for all five target triples (see docs/notes/sdk-architecture.md).

Crates: pyo3 (feature extension-module), maturin (build tool)

Critical tests:

• pdftract.extract("test.pdf") returns a dict with correct metadata.page_count
• pdftract.extract_text("test.pdf") returns a plain-text string
• pdftract.extract("nonexistent.pdf") raises PdftractError
• pdftract.extract("encrypted.pdf") raises EncryptionError
• Python threading: 4 threads each extracting different PDFs simultaneously; no deadlock

6.4 HTTP Serve Mode

Implement pdftract serve --port PORT. Requires --features serve at compile time (axum + tokio are not in the default build — they add ~2 MB to the binary). The pre-built release binaries for the serve Docker image are compiled with --features ocr,serve.

Endpoints:

Method	Path	Request	Response
POST	/extract	multipart/form-data file=<pdf> + optional form fields for options	JSON extraction result
POST	/extract/text	same	text/plain body
POST	/extract/stream	same	NDJSON stream (Content-Type: application/x-ndjson)
GET	/health	none	{"status":"ok","version":"x.y.z"}

Options via form fields: ocr=true, ocr_language=eng,fra, readability_threshold=0.5

Error responses: HTTP 400 for bad request (no file field, unsupported content type); HTTP 422 for extraction error (encrypted file, corrupt file); HTTP 500 for internal error. Response body is {"error":"code","message":"..."}.

Concurrency: axum handles concurrent requests; rayon thread pool is shared across all requests. No per-request thread spawning.

Request size limit: Default 256 MB; configurable via --max-upload-mb.

Crates: axum, tokio, tower-http (for RequestBodyLimit, TraceLayer), multer (multipart parsing)

Critical tests:

• curl -F file=@test.pdf http://localhost:8080/extract: valid JSON response
• File exceeding size limit: HTTP 413 response
• Concurrent requests with 8 simultaneous PDFs: all complete correctly
• /health endpoint: 200 OK, even while extractions are in progress

---

Phase 7: Advanced Features

Goal: StructTree exploitation, table detection, AcroForm/XFA, attachments, signatures.
Complexity: Medium–Complex per feature
Estimate: 4–5 weeks (features developed independently; can be parallelized across developers)
Depends on: Phase 6 complete

7.1 StructTree Exploitation (Tagged PDF)

Use the PDF structure tree as the authoritative reading order for tagged documents.

Implementation:

1. From document catalog /StructTreeRoot, load the root StructElem
2. Walk the structure tree depth-first; at each StructElem, record the element type (mapped via /RoleMap if non-standard), the /ActualText attribute (overrides extracted text if present), the /Alt attribute (alternative text for figures), and the /Lang attribute (BCP-47 language tag)
3. For each StructElem, collect its MCID references: each marked content sequence (identified by its MCID from Phase 3.4) is assigned to its owning StructElem via the ParentTree
4. Build the block list by traversing the structure tree in document order; each StructElem maps to one block; its constituent MCIDs provide the spans in reading order
5. Map structure element types to block kinds: P → paragraph, H/H1–H6 → heading with level, Table → table, L/LI → list, Figure → figure, Artifact → suppressed (not emitted in output)

Validation: If MarkInfo /Suspects true, fall back to XY-cut for any page where the structure tree coverage is less than 80% of extracted glyphs.

reading_order_algorithm: Set to "struct_tree" when used.

Crates: None beyond Phase 1 parser

Critical tests:

• Word-generated tagged PDF: heading levels correctly extracted (H1/H2 map to level 1/2)
• Tagged PDF with /ActualText on a ligature: ActualText value used, not glyph-decoded text
• Tagged PDF with /Artifact marked content: artifact glyphs excluded from output
• PDF with Suspects true: falls back to XY-cut, reading_order_algorithm = "xy_cut"

7.2 Table Detection and Structure Reconstruction

Detect tables and reconstruct cell structure.

Detection pipeline:

1. Line-based detection: Collect all horizontal and vertical path segments from the content stream (operators m/l/S, re/S, re/f). Cluster collinear segments. Find intersection points. Build grid from intersections. See docs/research/table-structure-reconstruction.md for the full grid reconstruction algorithm.
2. Borderless table detection: If no ruling lines found, examine span alignment: if 3+ lines share identical x0 positions for multiple groups, treat as candidate columns. Require 3+ rows to confirm.
3. Cell content assignment: For each cell bbox, collect all spans whose centroid falls within the bbox. Assign to the cell.
4. Header row detection: First row is header if all cells have bold font or if StructTree marks the row as TH type.
5. Merged cell detection: Missing interior edge between two cells → colspan or rowspan; infer from geometry.

Output: Block with kind: "table" and a parallel table object in the page output with rows/cells as per the schema.

Crates: None (geometry is pure arithmetic)

Critical tests:

• 5×3 bordered table: all 15 cells extracted with correct text
• Merged header cell spanning 3 columns: colspan=3 in output
• Borderless two-column table: detected via alignment heuristic
• Table spanning two pages: detected and flagged (full reconstruction deferred to non-streaming mode)

7.3 Digital Signature Metadata

Extract digital signature field metadata.

Implementation: Walk AcroForm /Fields array looking for Sig-type fields (/FT /Sig). For each signature field, extract: /T (field name), /V (signature dict) → /Name (signer name), /M (signing date, ISO 8601), /Reason, /Location, /ByteRange (byte ranges signed, for coverage analysis), /SubFilter (signature format: adbe.pkcs7.detached, adbe.x509.rsa.sha1, etc.).

Validation: pdftract does NOT perform cryptographic validation (that requires the full certificate chain and OCSP/CRL infrastructure). Instead, report validation_status: "not_checked". A future version may integrate ring or openssl for validation.

Output: signatures array at document level per the output schema.

Crates: None beyond Phase 1 parser

Critical tests:

• PDF with two signature fields: both extracted with correct signer names and dates
• Signature field with no /V (unsigned): extracted with value: null
• /ByteRange coverage: correctly computed as fraction of file bytes signed

7.4 AcroForm and XFA Field Extraction

Extract interactive form field definitions and current values.

AcroForm:

• Walk /Fields recursively (fields may be nested in /Kids)
• For each field: /T (partial name), /FT (type: Tx/Btn/Ch/Sig), /V (current value), /DV (default value), /Ff (flags: required, read-only, multi-line), /Rect (bbox)
• Tx fields: /V is a string
• Btn fields: /V is a name (the selected appearance state); compute is_checked
• Ch fields: /V is selected option; /Opt array lists all options
• Construct full field names by joining partial names with .

XFA:

• If /AcroForm /XFA is present, parse the XFA XML stream(s) (either single stream or array of named streams concatenated as XML)
• Walk the XFA data model to extract field values from <field> elements; use the XFA field name as the key
• If both AcroForm and XFA are present, prefer XFA values for overlapping fields

Crates: quick-xml (XFA parsing)

Critical tests:

• PDF with text field, checkbox, and dropdown: all three types extracted with correct values
• Nested field hierarchy: full dot-separated name constructed correctly
• XFA-only form: all field values extracted from XFA XML
• Hybrid XFA+AcroForm: XFA values preferred

7.5 Portfolio and Attachment Extraction

Extract embedded files from PDF portfolios and /EmbeddedFiles name trees.

Implementation:

• Locate the /EmbeddedFiles name tree in the catalog /Names dictionary
• Walk the name tree leaves, each yielding a Filespec dictionary
• From each Filespec: /F or /UF (filename), /Desc (description), /Type /Filespec, /EF dict → /F stream (the embedded file data)
• From the EF stream dictionary: /Subtype (MIME type hint), /Params dict → /Size, /CreationDate, /ModDate, /CheckSum
• Decode the stream (applying its filters)

Size limit: If attachment stream decoded size > 50 MB, include metadata only and set data: null with a truncated: true flag.

Portfolio navigator: Check for /Collection entry in catalog; if present, extract portfolio schema and sort fields for richer metadata.

Output: attachments array at document level.

Crates: None beyond Phase 1 parser and stream decoder

Critical tests:

• PDF with 3 embedded files of different MIME types: all three extracted with correct filenames and sizes
• Attachment with no /Desc: description is null (not empty string)
• Attachment exceeding size limit: metadata present, data: null, truncated: true

---

Cross-Cutting: Test Infrastructure

Tests are organized into three tiers:

Tier 1: Unit Tests (in-crate #[test])

Each module has unit tests covering the critical test cases listed per phase above. These run with cargo test and have no external dependencies.

Target: 100% of public function surfaces; all error paths exercised.

Tier 2: Integration Tests (tests/ directory)

Integration tests use a corpus of reference PDFs stored in tests/fixtures/. Each fixture has a corresponding expected-output JSON file. Tests verify:

• Exact text content match (for clean vector PDFs)
• Schema validity (all output against JSON Schema)
• Performance: extraction of a 100-page vector PDF completes in < 3 seconds on a 4-core CI machine (failure = CI block)

Fixture categories:

• tests/fixtures/vector/: clean LaTeX, Word, InDesign outputs
• tests/fixtures/scanned/: physical scans at various DPIs and skew angles
• tests/fixtures/cjk/: Chinese, Japanese, Korean documents
• tests/fixtures/malformed/: truncated, corrupt xref, circular references
• tests/fixtures/encrypted/: AES-128, AES-256, RC4 encrypted
• tests/fixtures/forms/: AcroForm and XFA documents
• tests/fixtures/tagged/: PDF/UA and PDF/A-a tagged documents

Tier 3: Regression Corpus (CI only)

A private corpus of 500 real-world PDFs from diverse sources runs on every PR. Output is compared against a golden snapshot using a character-level diff. Any regression > 0.5% character error rate blocks the PR.

Tier 4: Competitive Benchmarks (CI, tracked over time)

Benchmark suite runs pdftract, pdfminer.six, pypdf, and pdfplumber against identical fixture PDFs on the same CI machine. Results are stored as a JSON artifact per commit so regressions are detectable.

Metrics tracked per tool per fixture:

• Wall-clock extraction time (mean of 5 runs)
• Peak RSS (resident set size)
• Character error rate vs. ground truth
• Reading order correctness score

Minimum passing bar (blocks PR if missed):

• pdftract must be ≥ 5× faster than pdfminer.six on vector PDFs
• pdftract CER must be ≤ pdfminer.six CER on all fixture categories
• pdftract binary (default features) must be ≤ 4 MB stripped

Benchmark fixtures (tests/fixtures/bench/):

• vector-10.pdf, vector-100.pdf: clean LaTeX output
• cjk-20.pdf: mixed CJK
• two-column-academic.pdf: multi-column reading order
• scanned-5.pdf: physical scan (OCR path only in pdftract)

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Phase Dependencies and Sequencing

Phase 1 (Core Parser)
  └─► Phase 2 (Font Pipeline)
        └─► Phase 3 (Content Stream)
              └─► Phase 4 (Text Assembly)
                    ├─ 4.7 Readability Validation ← feeds back into 5.1 page classification
                    └─► Phase 5 (OCR)       ← Scanned PDFs work here; 4.7 escalates broken-vector pages here
                          └─► Phase 6 (API) ← PyO3, HTTP, full JSON schema
                                └─► Phase 7 (Advanced)
                                      ├─ 7.1 StructTree (independent)
                                      ├─ 7.2 Tables (independent)
                                      ├─ 7.3 Signatures (independent)
                                      ├─ 7.4 Forms (independent)
                                      └─ 7.5 Attachments (independent)

Phase 7 sub-tasks are independent of each other and can be assigned to separate developers once Phase 6 is complete.

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Release Milestones

Milestone	Phases Complete	Capability
v0.1.0 (Alpha)	1–4 (incl. 4.7)	Vector PDF extraction with readability validation; plain text and JSON output; CLI only; all three primary objective targets must pass
v0.2.0 (Beta)	1–5	+ Scanned PDF OCR; all page classes handled; competitive benchmark suite green
v0.3.0 (RC)	1–6	+ PyO3 bindings; HTTP serve; full JSON schema; NDJSON streaming
v1.0.0 (Stable)	1–7	+ StructTree; tables; forms; signatures; attachments

Binary releases for all five target triples are published to GitHub Releases on every milestone tag. The PyO3 wheel is published to PyPI. The CLI binary is the sole dependency for the subprocess-based SDKs documented in docs/notes/sdk-invocation.md.