pdf_oxide 0.3.67

The fastest Rust PDF library with text extraction: 0.8ms mean, 100% pass rate on 3,830 PDFs. 5× faster than pdf_extract, 17× faster than oxidize_pdf. Extract, create, and edit PDFs.
Documentation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
//! Region classification: prose / reference / table / form discrimination.
//!
//! # Why this module exists
//!
//! Untagged multi-column scientific papers (PMC/arXiv) carry **no** spec
//! reading order (ISO 32000-1 §14.8.2.3.1), so the geometric reading-order
//! heuristics in [`crate::document`] must reconstruct column order. The single
//! blocker is a **discrimination problem**: the gates that decide "reorder this
//! page column-major" cannot tell a two-column *prose* / *reference* body from a
//! *table* / *form* using thresholds alone. Loosen a gate to admit ragged
//! reference columns and it swallows tables; tighten it to protect tables and it
//! rejects the references. Prior threshold-only attempts were each reverted by
//! the corpus sweep on `google_doc_document.pdf`'s population table.
//!
//! The seed of the right answer already lived in
//! [`crate::pipeline::reading_order::xycut`]'s private `classify_region_kind`
//! (Prose / Table / Mixed via per-line `mean_chars` + narrow/wide line shape).
//! This module promotes that idea to a **first-class, page-level, unit-tested
//! primitive** and adds the two classes that seed was missing:
//!
//! * [`RegionClass::Reference`] — ragged two-column reference lists (numbered or
//!   hanging-indent entries with ragged right edges). Treated like prose by the
//!   reorder gates (reorder column-major), but named distinctly so a future pass
//!   can apply reference-specific entry grouping.
//! * [`RegionClass::Form`] — label / value rows (a large intra-line gap with text
//!   on both sides), the IRS-form shape that a relaxed prose gate would otherwise
//!   mis-read as a prose gutter.
//!
//! # Contract
//!
//! [`classify_region`] is a **pure read** (never mutates spans) and returns
//! [`RegionClass::Mixed`] on any ambiguity. Callers gate on the *class*, so a
//! misclassification degrades gracefully to the pre-existing geometric behaviour
//! rather than corrupting output.

use crate::layout::TextSpan;

/// Coarse structural class of a contiguous block of text spans.
///
/// Reorder gates admit `Prose` / `Reference` (reorder column-major) and reject
/// `Table` / `Form` (leave the existing row-major / cell handling alone).
/// `Mixed` means "not confidently any of the above" → callers fall back to their
/// prior behaviour.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RegionClass {
    /// Tall stack of wide lines, or narrow column lines carrying substantial
    /// prose content per line. Safe to reorder column-major.
    Prose,
    /// Ragged reference / bibliography column: numbered entries (`12.` / `[12]`)
    /// or hanging-indent entries with ragged-right tail lines. Reorder
    /// column-major like prose.
    Reference,
    /// Short cells in a grid (low mean characters per line, narrow-dominant).
    /// The canonical `google_doc_document.pdf` population table. Tight column
    /// reorder here corrupts cell ordering — do NOT treat as prose.
    Table,
    /// Label / value rows (a large intra-line gap with text on both sides), e.g.
    /// an IRS tax form. Do NOT treat as a prose gutter.
    Form,
    /// Too few lines, mixed shapes, or otherwise not confidently classifiable.
    Mixed,
}

impl RegionClass {
    /// True for the classes the column-reorder gates should accept (a 2-column
    /// body whose halves are both prose/reference is read column-major).
    pub fn is_reorderable_column(self) -> bool {
        matches!(self, RegionClass::Prose | RegionClass::Reference)
    }
}

/// Per-line aggregate built during classification.
struct LineStat {
    top: f32,
    left: f32,
    right: f32,
    /// Non-whitespace character count across all spans on the line.
    nonws_chars: usize,
    /// Trimmed text of the leftmost span on the line (for numbered-entry shape).
    lead_text: String,
    /// Left edges of every span on the line (sorted ascending), for intra-line
    /// gap (form) detection.
    span_lefts: Vec<f32>,
    /// Right edges paired with `span_lefts` order, for the same.
    span_rights: Vec<f32>,
}

/// Classify the block formed by `spans[indices]`.
///
/// Pure read. Returns [`RegionClass::Mixed`] whenever the block is too small or
/// the shape is ambiguous, so callers safely fall back to prior behaviour.
pub fn classify_region(spans: &[TextSpan], indices: &[usize]) -> RegionClass {
    // --- cheap shape guards (mirror xycut's classify_region_kind) ---
    if indices.len() < 6 {
        return RegionClass::Mixed;
    }
    let mut x_min = f32::MAX;
    let mut x_max = f32::MIN;
    for &i in indices {
        x_min = x_min.min(spans[i].bbox.left());
        x_max = x_max.max(spans[i].bbox.right());
    }
    let region_width = x_max - x_min;
    if region_width <= 10.0 {
        return RegionClass::Mixed;
    }

    // Median glyph height drives the line-clustering Y tolerance.
    let med_h = median_height(spans, indices).max(1.0);

    let lines = cluster_lines(spans, indices, med_h);
    let line_count = lines.len();
    if line_count < 6 {
        // Headings, captions, single paragraphs — leave to default behaviour.
        return RegionClass::Mixed;
    }

    // --- per-line statistics ---
    let mut total_chars = 0usize;
    let mut wide_lines = 0usize;
    let mut numbered_lines = 0usize;
    let mut form_lines = 0usize;
    let mut left_edges: Vec<f32> = Vec::with_capacity(line_count);
    for l in &lines {
        total_chars += l.nonws_chars;
        let extent = (l.right - l.left).max(0.0);
        if extent >= region_width * 0.6 {
            wide_lines += 1;
        }
        if starts_numbered_entry(&l.lead_text) {
            numbered_lines += 1;
        }
        if line_has_label_value_gap(l, region_width) {
            form_lines += 1;
        }
        left_edges.push(l.left);
    }
    let mean_chars = total_chars as f32 / line_count as f32;
    let mostly_wide = wide_lines * 2 > line_count;
    let numbered_frac = numbered_lines as f32 / line_count as f32;
    let form_frac = form_lines as f32 / line_count as f32;

    // --- decision ladder (specific → general; default Mixed) ---
    //
    // Table and Form both mean "do NOT reorder as prose", so a fuzzy Table/Form
    // boundary is harmless — only the {Prose,Reference} vs {Table,Form} split is
    // load-bearing for the reorder gates.

    // TABLE: short content per line. This is the robust grid signal — a prose or
    // reference column always carries substantial text per line, so it never
    // falls this low, whereas the google_doc_document population table's
    // digit-only cells (≤ 7 chars) and any short-cell grid land here regardless
    // of how wide the row spans.
    if mean_chars < 10.0 {
        return RegionClass::Table;
    }

    // FORM: a large fraction of lines are label … value rows (a wide interior gap
    // with text on both sides). Distinguishes IRS tax forms — whose label text is
    // long enough to otherwise read as prose — from a real prose body.
    if form_frac >= 0.4 {
        return RegionClass::Form;
    }

    // REFERENCE: numbered entries, or a hanging-indent two-level left edge, with
    // enough text per line to exclude table cells. Treated like prose downstream.
    if mean_chars > 12.0 && (numbered_frac >= 0.3 || has_hanging_indent(&left_edges, med_h)) {
        return RegionClass::Reference;
    }

    // PROSE: a tall stack of wide lines with substantial content per line (the
    // xycut `mean_chars > 20` body signal). The `mostly_narrow` half-column verse
    // path xycut also admits is deliberately NOT reproduced here: it risks
    // pulling a wide-number table into the reorder gate, and our academic targets
    // are wide-line column bodies, not short-verse editions (those degrade to
    // Mixed = prior behaviour, which is safe).
    if mean_chars > 20.0 && mostly_wide {
        return RegionClass::Prose;
    }

    RegionClass::Mixed
}

/// Median of the spans' glyph heights (linear-time enough for blocks).
fn median_height(spans: &[TextSpan], indices: &[usize]) -> f32 {
    let mut hs: Vec<f32> = indices
        .iter()
        .map(|&i| spans[i].bbox.height.abs())
        .filter(|h| *h > 0.0)
        .collect();
    if hs.is_empty() {
        return 1.0;
    }
    hs.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
    hs[hs.len() / 2]
}

/// Cluster spans into baseline lines (top→bottom), tolerant of small Y jitter.
fn cluster_lines(spans: &[TextSpan], indices: &[usize], med_h: f32) -> Vec<LineStat> {
    let mut order: Vec<usize> = indices.to_vec();
    order.sort_by(|&a, &b| {
        spans[a]
            .bbox
            .top()
            .partial_cmp(&spans[b].bbox.top())
            .unwrap_or(std::cmp::Ordering::Equal)
            .then_with(|| {
                spans[a]
                    .bbox
                    .left()
                    .partial_cmp(&spans[b].bbox.left())
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
    });

    let tol = med_h * 0.6;
    let mut lines: Vec<LineStat> = Vec::new();
    for &i in &order {
        let s = &spans[i];
        let nonws = s.text.chars().filter(|c| !c.is_whitespace()).count();
        match lines.last_mut() {
            Some(l) if (s.bbox.top() - l.top).abs() <= tol => {
                l.left = l.left.min(s.bbox.left());
                l.right = l.right.max(s.bbox.right());
                l.nonws_chars += nonws;
                if s.bbox.left() < l.span_lefts[0] {
                    // New leftmost span on this line → it owns the lead text.
                    l.lead_text = s.text.trim_start().to_string();
                }
                l.span_lefts.push(s.bbox.left());
                l.span_rights.push(s.bbox.right());
            },
            _ => lines.push(LineStat {
                top: s.bbox.top(),
                left: s.bbox.left(),
                right: s.bbox.right(),
                nonws_chars: nonws,
                lead_text: s.text.trim_start().to_string(),
                span_lefts: vec![s.bbox.left()],
                span_rights: vec![s.bbox.right()],
            }),
        }
    }
    // Keep `span_lefts`/`span_rights` paired and left-sorted for gap analysis.
    for l in &mut lines {
        let mut paired: Vec<(f32, f32)> = l
            .span_lefts
            .iter()
            .copied()
            .zip(l.span_rights.iter().copied())
            .collect();
        paired.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal));
        l.span_lefts = paired.iter().map(|p| p.0).collect();
        l.span_rights = paired.iter().map(|p| p.1).collect();
    }
    lines
}

/// True when a line's leading text begins a numbered/bracketed reference entry:
/// `12.`, `12)`, `[12]`, `(12)`.
fn starts_numbered_entry(lead: &str) -> bool {
    let b = lead.as_bytes();
    if b.is_empty() {
        return false;
    }
    // `[12]` / `[12` / `(12`
    if (b[0] == b'[' || b[0] == b'(') && b.get(1).is_some_and(u8::is_ascii_digit) {
        return true;
    }
    // `12.` / `12)` — 1..=3 leading digits then a `.` or `)`.
    let digits = b.iter().take(4).take_while(|c| c.is_ascii_digit()).count();
    if (1..=3).contains(&digits) {
        if let Some(&next) = b.get(digits) {
            return next == b'.' || next == b')';
        }
    }
    false
}

/// True when a line is a label … value row: one large interior horizontal gap
/// (≥ 0.25 · region width) with real text on both sides — the IRS-form shape.
fn line_has_label_value_gap(l: &LineStat, region_width: f32) -> bool {
    if l.span_lefts.len() < 2 {
        return false;
    }
    let threshold = region_width * 0.25;
    // Largest gap between the right edge of one span and the left of the next.
    for w in 1..l.span_lefts.len() {
        let gap = l.span_lefts[w] - l.span_rights[w - 1];
        if gap >= threshold {
            return true;
        }
    }
    false
}

/// Detect a hanging-indent two-level left edge: a primary entry-start edge `l0`
/// and a secondary continuation edge `l0 + δ` (δ ∈ ~[0.8, 5]·med_h), both
/// carrying a meaningful share of lines. Reference lists and prose with
/// first-line indents both produce this bimodality — for the reorder gate that
/// distinction does not matter (both reorder column-major).
fn has_hanging_indent(left_edges: &[f32], med_h: f32) -> bool {
    if left_edges.len() < 6 {
        return false;
    }
    let l0 = left_edges.iter().copied().fold(f32::MAX, f32::min);
    let near_tol = med_h * 0.5;
    let lo_band = left_edges
        .iter()
        .filter(|&&x| (x - l0).abs() <= near_tol)
        .count();
    // Continuation band: lines indented δ ∈ [0.8, 5]·med_h past l0.
    let hi_band = left_edges
        .iter()
        .filter(|&&x| {
            let d = x - l0;
            d >= med_h * 0.8 && d <= med_h * 5.0
        })
        .count();
    let n = left_edges.len();
    // Both bands must be substantial (≥ 25% of lines each).
    lo_band * 4 >= n && hi_band * 4 >= n
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::geometry::Rect;

    /// Build a minimal span at (left, top) with width/height and given text.
    fn span(text: &str, left: f32, top: f32, width: f32, height: f32) -> TextSpan {
        TextSpan {
            text: text.to_string(),
            bbox: Rect::new(left, top, width, height),
            font_size: height,
            ..Default::default()
        }
    }

    /// A line of prose: one wide span ~`chars` characters long at `top`.
    fn prose_line(top: f32, left: f32, chars: usize) -> TextSpan {
        let text: String = "x".repeat(chars);
        span(&text, left, top, chars as f32 * 5.0, 10.0)
    }

    fn classify(spans: &[TextSpan]) -> RegionClass {
        let idx: Vec<usize> = (0..spans.len()).collect();
        classify_region(spans, &idx)
    }

    #[test]
    fn classify_dense_results_is_prose() {
        // 10 wide lines, ~40 chars each → mean_chars > 20, mostly wide.
        let spans: Vec<TextSpan> = (0..10)
            .map(|i| prose_line(i as f32 * 12.0, 0.0, 40))
            .collect();
        assert_eq!(classify(&spans), RegionClass::Prose);
    }

    #[test]
    fn classify_numbered_references_is_reference() {
        // Each line starts "12. Author, Title ..." → numbered entries.
        let spans: Vec<TextSpan> = (0..8)
            .map(|i| {
                let t = format!("{}. Author A, Title of the work, Journal", i + 1);
                span(&t, 0.0, i as f32 * 12.0, 180.0, 10.0)
            })
            .collect();
        assert_eq!(classify(&spans), RegionClass::Reference);
    }

    #[test]
    fn classify_hanging_indent_references_is_reference() {
        // Two-line entries: entry start at x=0, continuation indented to x=15.
        let mut spans = Vec::new();
        for e in 0..4 {
            let base = e as f32 * 24.0;
            spans.push(span(
                "Smith J, Some long reference entry title here",
                0.0,
                base,
                200.0,
                10.0,
            ));
            spans.push(span(
                "continuation of the reference line indented",
                15.0,
                base + 12.0,
                180.0,
                10.0,
            ));
        }
        assert_eq!(classify(&spans), RegionClass::Reference);
    }

    #[test]
    fn classify_table_cells_is_table() {
        // 12 short numeric cells across 6 rows, narrow → mean_chars < 8.
        let mut spans = Vec::new();
        for r in 0..6 {
            spans.push(span("12.3", 0.0, r as f32 * 12.0, 18.0, 10.0));
            spans.push(span("45.6", 60.0, r as f32 * 12.0, 18.0, 10.0));
        }
        assert_eq!(classify(&spans), RegionClass::Table);
    }

    #[test]
    fn classify_form_label_value_is_form() {
        // Label at left, value far right → large interior gap on every line.
        let spans: Vec<TextSpan> = (0..8)
            .map(|i| {
                let mut label =
                    span("Wages, salaries, tips, etc.", 0.0, i as f32 * 12.0, 90.0, 10.0);
                label.text = "Wages, salaries, tips".to_string();
                label
            })
            .collect();
        // Add right-aligned value spans creating the gap.
        let mut all = Vec::new();
        for (i, l) in spans.into_iter().enumerate() {
            all.push(l);
            all.push(span("1,234", 200.0, i as f32 * 12.0, 30.0, 10.0));
        }
        assert_eq!(classify(&all), RegionClass::Form);
    }

    #[test]
    fn classify_single_paragraph_is_mixed() {
        // Only 4 lines → below the 6-line substantiality floor.
        let spans: Vec<TextSpan> = (0..4)
            .map(|i| prose_line(i as f32 * 12.0, 0.0, 40))
            .collect();
        assert_eq!(classify(&spans), RegionClass::Mixed);
    }

    #[test]
    fn classify_empty_or_tiny_is_mixed() {
        assert_eq!(classify(&[]), RegionClass::Mixed);
        let spans: Vec<TextSpan> = (0..3)
            .map(|i| prose_line(i as f32 * 12.0, 0.0, 10))
            .collect();
        assert_eq!(classify(&spans), RegionClass::Mixed);
    }
}