Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Abimael MartellandClaude Fable 5
|
cc889ac177 |
+10
-492
@@ -39,6 +39,7 @@ pub mod markdown;
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pub mod process_mode;
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pub mod structure_tree;
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pub mod tables;
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mod text_quality;
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pub mod text_utils;
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pub mod tounicode;
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pub mod types;
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@@ -60,6 +61,10 @@ pub use types::{LayoutComplexity, PdfLine, PdfRect, TextItem};
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use lopdf::Document;
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use std::collections::{BTreeMap, HashMap, HashSet};
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use std::path::Path;
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use text_quality::{
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analyze_text_quality, detect_encoding_issues, is_cid_garbage, is_garbage_text,
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region_items_have_decoding_issue,
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};
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use tounicode::FontCMaps;
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/// OCR reason emitted when the extracted text layer appears garbled due to
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@@ -3681,283 +3686,15 @@ fn process_document(
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// Internal helpers
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// =========================================================================
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/// Detect broken font encodings in extracted markdown text.
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///
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/// Two heuristics:
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/// 1. **U+FFFD**: Any replacement character indicates decode failures.
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/// 2. **Dollar-as-space**: Pattern like `Word$Word$Word` where `$` is used as a
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/// word separator due to broken ToUnicode CMaps. Triggers when either:
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/// - More than 50% of `$` are between letters (clear substitution pattern), OR
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/// - More than 20 letter-dollar-letter occurrences (even if some `$` are also
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/// used as trailing/leading separators, 20+ is far beyond normal financial text).
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fn detect_encoding_issues(markdown: &str) -> bool {
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// Heuristic 1: U+FFFD replacement characters
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if markdown.contains('\u{FFFD}') {
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return true;
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}
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// Heuristic 2: dollar-as-space pattern
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if has_dollar_as_space_pattern(markdown) {
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return true;
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}
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// Heuristic 3: substitution-cipher letter statistics (broken ToUnicode)
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let mut stats = CipherGarbleStats::default();
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stats.add_text(markdown);
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stats.looks_garbled()
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}
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fn has_dollar_as_space_pattern(markdown: &str) -> bool {
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let total_dollars = markdown.matches('$').count();
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if total_dollars > 10 {
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let bytes = markdown.as_bytes();
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let mut letter_dollar_letter = 0usize;
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for i in 1..bytes.len().saturating_sub(1) {
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if bytes[i] == b'$'
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&& bytes[i - 1].is_ascii_alphabetic()
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&& bytes[i + 1].is_ascii_alphabetic()
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{
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letter_dollar_letter += 1;
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}
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}
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if letter_dollar_letter > 20 || letter_dollar_letter * 2 > total_dollars {
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return true;
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}
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}
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false
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}
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/// English letter frequencies (percent, a–z). Used as a natural-language
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/// reference: every Latin-script language in the eval corpus (Swedish,
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/// Finnish, Turkish, German, romaji) scores ≥ 0.80 cosine similarity against
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/// it, while substitution-cipher text scores ~0.53.
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const ENGLISH_LETTER_FREQ: [f64; 26] = [
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8.2, 1.5, 2.8, 4.3, 12.7, 2.2, 2.0, 6.1, 7.0, 0.15, 0.8, 4.0, 2.4, 6.7, 7.5, 1.9, 0.1, 6.0,
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6.3, 9.1, 2.8, 1.0, 2.4, 0.15, 2.0, 0.07,
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];
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/// Letter statistics for detecting substitution-cipher garbling: broken
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/// ToUnicode CMaps that shift every character by a per-range constant (e.g.
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/// `Certificate` extracted as `8VceZWZTReV`). Such text is 100% printable
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/// ASCII with word-like token lengths, so it defeats `is_garbage_text` and
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/// produces no replacement characters — it needs its own discriminator.
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#[derive(Debug, Default)]
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struct CipherGarbleStats {
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/// Case-folded ASCII letter histogram.
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letter_counts: [u32; 26],
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ascii_letters: usize,
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ascii_vowels: usize,
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/// Accented Latin letters (Latin-1 Supplement through Latin Extended-B,
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/// plus Latin Extended Additional). Count toward Latin dominance only.
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latin_ext_letters: usize,
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non_latin_letters: usize,
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/// Adjacent ASCII-letter pairs, and how many of them switch from
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/// lowercase straight to uppercase mid-word.
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letter_bigrams: usize,
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case_shift_bigrams: usize,
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}
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impl CipherGarbleStats {
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fn add_text(&mut self, text: &str) {
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let mut prev: Option<char> = None;
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for ch in text.chars() {
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if ch.is_ascii_alphabetic() {
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let idx = (ch.to_ascii_lowercase() as u8 - b'a') as usize;
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self.letter_counts[idx] += 1;
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self.ascii_letters += 1;
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if matches!(ch.to_ascii_lowercase(), 'a' | 'e' | 'i' | 'o' | 'u') {
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self.ascii_vowels += 1;
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}
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if let Some(p) = prev {
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self.letter_bigrams += 1;
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if p.is_ascii_lowercase() && ch.is_ascii_uppercase() {
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self.case_shift_bigrams += 1;
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}
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}
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prev = Some(ch);
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} else {
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if ch.is_alphabetic() {
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if matches!(ch as u32, 0xC0..=0x24F | 0x1E00..=0x1EFF) {
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self.latin_ext_letters += 1;
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} else {
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self.non_latin_letters += 1;
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}
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}
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prev = None;
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}
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}
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}
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/// Cosine similarity between the observed letter histogram and English
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/// letter frequencies. A shifted alphabet permutes the histogram, which
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/// destroys the similarity regardless of the shift amount.
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fn english_cosine(&self) -> f64 {
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if self.ascii_letters == 0 {
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return 1.0;
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}
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let n = self.ascii_letters as f64;
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let mut dot = 0.0;
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let mut norm_obs = 0.0;
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for (count, freq) in self.letter_counts.iter().zip(ENGLISH_LETTER_FREQ) {
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let p = *count as f64 / n;
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dot += p * freq;
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norm_obs += p * p;
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}
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let norm_en = ENGLISH_LETTER_FREQ
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.iter()
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.map(|f| f * f)
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.sum::<f64>()
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.sqrt();
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dot / (norm_obs.sqrt() * norm_en)
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}
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/// Cosine similarity between the observed histogram and English
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/// frequencies after sorting BOTH descending — i.e. comparing the *shape*
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/// of the frequency profile, ignoring which letter sits where. A
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/// substitution cipher is a bijection, so it preserves this shape exactly
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/// (att10k 0.97, arbitrary shifts 0.99) regardless of case or offset.
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/// Non-linguistic ASCII has a different profile: a small alphabet is far
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/// steeper (random DNA 0.74, hex dumps 0.81), so the shape diverges.
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fn english_shape_cosine(&self) -> f64 {
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if self.ascii_letters == 0 {
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return 1.0;
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}
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let n = self.ascii_letters as f64;
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let mut obs: [f64; 26] = std::array::from_fn(|i| self.letter_counts[i] as f64 / n);
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obs.sort_unstable_by(|a, b| b.total_cmp(a));
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let mut en = ENGLISH_LETTER_FREQ;
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en.sort_unstable_by(|a, b| b.total_cmp(a));
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let dot: f64 = obs.iter().zip(en).map(|(o, e)| o * e).sum();
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let norm_obs = obs.iter().map(|o| o * o).sum::<f64>().sqrt();
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let norm_en = en.iter().map(|e| e * e).sum::<f64>().sqrt();
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dot / (norm_obs * norm_en)
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}
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/// Thresholds validated against the 380-document pdf-evals snapshot
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/// corpus (0 false positives) and the garbled ParseBench `att10k` page
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/// (vowel ratio 0.245, case-shift rate 0.225, cosine 0.532). Closest
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/// legitimate document on each axis: vowel ratio 0.264 (circuit
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/// schematic), case-shift rate 0.021, cosine 0.801.
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fn looks_garbled(&self) -> bool {
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// Need a statistically meaningful, Latin-dominant sample.
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if self.ascii_letters < 200
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|| self.non_latin_letters > self.ascii_letters + self.latin_ext_letters
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{
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return false;
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}
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// Real Latin-script text keeps vowels above ~30% of letters even in
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// acronym- and part-number-heavy documents; shifted text starves them.
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let vowel_ratio = self.ascii_vowels as f64 / self.ascii_letters as f64;
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if vowel_ratio > 0.30 {
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return false;
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}
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// Signal 1: lowercase→uppercase transitions inside words. A shifted
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// lowercase alphabet straddles the ASCII uppercase block ('i'→'Z',
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// 't'→'e'), so garbled words flip case constantly. Real documents
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// stay ≤ 0.02 even with camelCase identifiers.
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let case_shifts = self.letter_bigrams >= 100
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&& self.case_shift_bigrams as f64 >= self.letter_bigrams as f64 * 0.10;
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// Signal 2: the histogram is a permutation of natural language — an
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// English-like frequency SHAPE (sorted cosine high) but with letters
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// in the wrong POSITIONS (unsorted cosine low). This is the signature
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// of a substitution cipher and is case-independent, so it catches
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// all-lowercase and all-uppercase shifts as well as case-straddling
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// ones. Genuinely non-linguistic ASCII that is merely "unlike English"
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// fails one of the two halves: DNA/hex dumps have too steep a profile
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// (shape cosine < 0.90), while protein sequences, ticker symbols and
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// base64 are not sufficiently unlike English in position (unsorted
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// cosine ≥ 0.60) — so none of them are routed to OCR.
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let permuted_language = self.english_cosine() < 0.60 && self.english_shape_cosine() >= 0.90;
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case_shifts || permuted_language
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}
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}
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#[derive(Debug, Default)]
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struct TextQualityReport {
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pages_needing_ocr: Vec<u32>,
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has_encoding_issues: bool,
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reasons_by_page: BTreeMap<u32, Vec<String>>,
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}
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#[derive(Debug, Default)]
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struct PageTextQualityEvidence {
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chars: usize,
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replacement_chars: usize,
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replacement_spans: usize,
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longest_replacement_run: usize,
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cipher_garble: CipherGarbleStats,
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}
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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enum TextSpanIssueKind {
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Replacement,
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Strong,
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}
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fn analyze_text_quality(items: &[TextItem]) -> TextQualityReport {
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let mut reasons_by_page = BTreeMap::new();
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let mut evidence_by_page = BTreeMap::<u32, PageTextQualityEvidence>::new();
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for item in items {
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if !matches!(item.item_type, crate::types::ItemType::Text) {
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continue;
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}
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let evidence = evidence_by_page.entry(item.page).or_default();
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evidence.chars += item.text.chars().filter(|ch| !ch.is_whitespace()).count();
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evidence.cipher_garble.add_text(&item.text);
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match text_span_decoding_issue_kind(&item.text) {
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Some(TextSpanIssueKind::Strong) => {
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add_ocr_reason(
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&mut reasons_by_page,
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item.page,
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OCR_REASON_SUSPECTED_GARBLED_TEXT,
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);
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}
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Some(TextSpanIssueKind::Replacement) => {
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let stats = replacement_text_stats(&item.text);
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evidence.replacement_chars += stats.0;
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evidence.replacement_spans += 1;
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evidence.longest_replacement_run = evidence.longest_replacement_run.max(stats.1);
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}
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None => {}
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}
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}
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for (page, evidence) in evidence_by_page {
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if reasons_by_page.contains_key(&page) {
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continue;
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}
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if page_replacement_evidence_needs_ocr(&evidence) || evidence.cipher_garble.looks_garbled()
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{
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add_ocr_reason(
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&mut reasons_by_page,
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page,
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OCR_REASON_SUSPECTED_GARBLED_TEXT,
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);
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}
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}
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let pages_needing_ocr: Vec<u32> = reasons_by_page.keys().copied().collect();
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TextQualityReport {
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has_encoding_issues: !pages_needing_ocr.is_empty(),
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pages_needing_ocr,
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reasons_by_page,
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}
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}
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fn suspected_garbled_reason() -> String {
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OCR_REASON_SUSPECTED_GARBLED_TEXT.to_string()
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}
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fn add_ocr_reason(reasons_by_page: &mut BTreeMap<u32, Vec<String>>, page: u32, reason: &str) {
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pub(crate) fn add_ocr_reason(
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reasons_by_page: &mut BTreeMap<u32, Vec<String>>,
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page: u32,
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reason: &str,
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) {
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let reasons = reasons_by_page.entry(page).or_default();
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if !reasons.iter().any(|existing| existing == reason) {
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reasons.push(reason.to_string());
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@@ -3989,225 +3726,6 @@ fn page_ocr_reasons_vec(reasons_by_page: BTreeMap<u32, Vec<String>>) -> Vec<Page
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.collect()
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}
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fn region_items_have_decoding_issue(items: &[TextItem]) -> bool {
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items.iter().any(|item| {
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matches!(item.item_type, crate::types::ItemType::Text)
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&& text_span_has_decoding_issue(&item.text)
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})
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}
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fn text_span_has_decoding_issue(text: &str) -> bool {
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text_span_decoding_issue_kind(text).is_some()
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}
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fn text_span_decoding_issue_kind(text: &str) -> Option<TextSpanIssueKind> {
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let text = text.trim();
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if text.is_empty() {
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return None;
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}
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if has_dollar_as_space_pattern(text)
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|| has_private_use_text_run(text)
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|| is_cid_garbage(text)
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|| has_cid_control_token(text)
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{
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return Some(TextSpanIssueKind::Strong);
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}
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if has_replacement_text_run(text) {
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return Some(TextSpanIssueKind::Replacement);
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}
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None
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}
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fn replacement_text_stats(text: &str) -> (usize, usize) {
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let mut replacement = 0usize;
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let mut current_run = 0usize;
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let mut longest_run = 0usize;
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for ch in text.chars() {
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if ch == '\u{FFFD}' {
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replacement += 1;
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current_run += 1;
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longest_run = longest_run.max(current_run);
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} else {
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current_run = 0;
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}
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}
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(replacement, longest_run)
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}
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fn page_replacement_evidence_needs_ocr(evidence: &PageTextQualityEvidence) -> bool {
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if evidence.replacement_chars == 0 || evidence.chars == 0 {
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return false;
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}
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// If the entire page is only a short broken text layer, even a short
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// replacement run is enough evidence. On otherwise text-heavy pages,
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// require density so math formulas do not force full-page OCR.
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if evidence.chars <= 80 && evidence.longest_replacement_run >= 2 {
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return true;
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}
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let replacement_density_bps = evidence.replacement_chars * 10_000 / evidence.chars;
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let enough_bad_text = evidence.replacement_chars >= 12 && replacement_density_bps >= 500;
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let repeated_bad_spans = evidence.replacement_spans >= 3 && replacement_density_bps >= 250;
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let long_bad_run = evidence.longest_replacement_run >= 8 && replacement_density_bps >= 250;
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enough_bad_text || repeated_bad_spans || long_bad_run
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}
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fn has_replacement_text_run(text: &str) -> bool {
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let (replacement, longest_run) = replacement_text_stats(text);
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longest_run >= 2 || replacement >= 3
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}
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fn has_private_use_text_run(text: &str) -> bool {
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let mut total = 0usize;
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let mut private_use = 0usize;
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let mut current_run = 0usize;
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let mut longest_run = 0usize;
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for ch in text.chars() {
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if ch.is_whitespace() {
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current_run = 0;
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continue;
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}
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total += 1;
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if is_private_use_char(ch) {
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private_use += 1;
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current_run += 1;
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longest_run = longest_run.max(current_run);
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} else {
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current_run = 0;
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}
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}
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if private_use == 0 {
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return false;
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}
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longest_run >= 3 || (total >= 5 && private_use >= 2 && private_use * 2 >= total)
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}
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fn has_cid_control_token(text: &str) -> bool {
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text.split_whitespace().any(token_has_cid_control)
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}
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fn token_has_cid_control(token: &str) -> bool {
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let mut total = 0usize;
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let mut c1_control = 0usize;
|
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for ch in token.chars() {
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total += 1;
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if ('\u{0080}'..='\u{009F}').contains(&ch) {
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c1_control += 1;
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}
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}
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total >= 5 && c1_control >= 2 && c1_control * 20 >= total
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}
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fn is_private_use_char(ch: char) -> bool {
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matches!(
|
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ch as u32,
|
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0xE000..=0xF8FF | 0xF0000..=0xFFFFD | 0x100000..=0x10FFFD
|
||||
)
|
||||
}
|
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|
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/// Check if extracted text is predominantly garbage (non-alphanumeric).
|
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///
|
||||
/// Broken font encodings produce text like "----1-.-.-.___ --.-. .._ I_---."
|
||||
/// where most characters are punctuation/symbols. Real text in any language
|
||||
/// has >50% alphanumeric characters.
|
||||
fn is_garbage_text(markdown: &str) -> bool {
|
||||
let mut alphanum = 0usize;
|
||||
let mut non_alphanum = 0usize;
|
||||
|
||||
let chars: Vec<char> = markdown.chars().collect();
|
||||
let mut i = 0usize;
|
||||
while i < chars.len() {
|
||||
let ch = chars[i];
|
||||
let mut run_end = i + 1;
|
||||
while run_end < chars.len() && chars[run_end] == ch {
|
||||
run_end += 1;
|
||||
}
|
||||
|
||||
let is_decorative_leader = matches!(ch, '.' | '_' | '·') && run_end - i >= 3;
|
||||
if !is_decorative_leader {
|
||||
for &run_ch in &chars[i..run_end] {
|
||||
if run_ch.is_whitespace() {
|
||||
continue;
|
||||
}
|
||||
// Skip markdown syntax chars that we add (not from the PDF)
|
||||
if matches!(run_ch, '#' | '*' | '|' | '-' | '\n') {
|
||||
continue;
|
||||
}
|
||||
if run_ch.is_alphanumeric() {
|
||||
alphanum += 1;
|
||||
} else {
|
||||
non_alphanum += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
i = run_end;
|
||||
}
|
||||
|
||||
let total = alphanum + non_alphanum;
|
||||
total >= 50 && alphanum * 2 < total
|
||||
}
|
||||
|
||||
/// Detect garbage from failed CID-to-Unicode mapping on Identity-H fonts.
|
||||
///
|
||||
/// When CID values don't correspond to Unicode codepoints, the raw bytes often
|
||||
/// produce characters in the C1 control range (U+0080–U+009F) or Private Use
|
||||
/// Area, mixed with random Latin Extended characters. Valid text in any
|
||||
/// language almost never contains C1 controls. We also fall back to the
|
||||
/// general `is_garbage_text` check for non-alphanumeric-heavy patterns.
|
||||
fn is_cid_garbage(text: &str) -> bool {
|
||||
if is_garbage_text(text) {
|
||||
return true;
|
||||
}
|
||||
let mut total = 0usize;
|
||||
let mut c1_control = 0usize;
|
||||
let mut high_latin = 0usize;
|
||||
for ch in text.chars() {
|
||||
if ch.is_whitespace() {
|
||||
continue;
|
||||
}
|
||||
total += 1;
|
||||
// C1 control characters (U+0080–U+009F) — almost never in real text
|
||||
if ch == '·' {
|
||||
continue;
|
||||
}
|
||||
if ('\u{0080}'..='\u{009F}').contains(&ch) {
|
||||
c1_control += 1;
|
||||
}
|
||||
// High Latin-1 (U+00A0–U+00FF) — legitimate in Western European text
|
||||
// but when combined with ASCII in CID passthrough, indicates mojibake
|
||||
// from CID values being misinterpreted as Latin-1 characters.
|
||||
if ('\u{00A0}'..='\u{00FF}').contains(&ch) {
|
||||
high_latin += 1;
|
||||
}
|
||||
}
|
||||
if total < 5 {
|
||||
return false;
|
||||
}
|
||||
// If ≥5% of non-whitespace chars are C1 controls, it's garbage
|
||||
if c1_control >= 2 && c1_control * 20 >= total {
|
||||
return true;
|
||||
}
|
||||
// If ≥40% of non-whitespace chars are high Latin-1 AND the text has few
|
||||
// ASCII letters, it's likely CID-as-Latin-1 mojibake (Japanese/CJK PDFs
|
||||
// where CID values 0x80-0xFF become accented Latin characters). Keep a
|
||||
// minimum length so short math tokens like "2×()×" do not route a clean
|
||||
// page to OCR.
|
||||
let ascii_letters = text.chars().filter(|c| c.is_ascii_alphabetic()).count();
|
||||
total >= 20 && high_latin * 5 >= total * 2 && ascii_letters * 3 < total
|
||||
}
|
||||
|
||||
/// Detect markdown tables with suspicious structure that suggest the heuristic
|
||||
/// missed/mangled rows or columns. Returns true when the caller should treat
|
||||
/// the result as `needs_ocr` and fall back to GPU OCR.
|
||||
|
||||
@@ -0,0 +1,520 @@
|
||||
//! Text-quality detection: deciding when an extracted text layer is too broken
|
||||
//! to serve and a page should fall back to OCR.
|
||||
//!
|
||||
//! Extraction can produce plausible-looking bytes that are actually garbage —
|
||||
//! failed CID→Unicode mappings, broken ToUnicode CMaps, mojibake. These
|
||||
//! detectors catch that and let callers set `needs_ocr`. They come in two
|
||||
//! layers, sharing the same primitives:
|
||||
//!
|
||||
//! - **Markdown-level** ([`detect_encoding_issues`], [`is_garbage_text`],
|
||||
//! [`is_cid_garbage`]) run on a page's final markdown string. Used as a
|
||||
//! backstop on the region-extraction and whole-document paths.
|
||||
//! - **Item/span-level** ([`analyze_text_quality`],
|
||||
//! [`region_items_have_decoding_issue`]) run on individual `TextItem`s and
|
||||
//! accumulate per-page evidence, so localized garbled spans on an otherwise
|
||||
//! clean page are caught without a single span having to condemn the page.
|
||||
//!
|
||||
//! Detection classes, roughly by signal:
|
||||
//! - **Replacement runs**: U+FFFD clusters ([`has_replacement_text_run`]).
|
||||
//! - **Private-use / C1-control runs**: CID passthrough landing in PUA or the
|
||||
//! C1 block ([`has_private_use_text_run`], [`has_cid_control_token`]).
|
||||
//! - **Dollar-as-space**: `Word$Word$Word` from broken CMaps
|
||||
//! ([`has_dollar_as_space_pattern`]).
|
||||
//! - **Non-alphanumeric dominance**: symbol soup ([`is_garbage_text`]).
|
||||
//! - **Substitution-cipher letter statistics**: pure-ASCII output whose letter
|
||||
//! distribution is a permutation of natural language ([`CipherGarbleStats`]).
|
||||
|
||||
use crate::types::TextItem;
|
||||
use crate::{add_ocr_reason, OCR_REASON_SUSPECTED_GARBLED_TEXT};
|
||||
use std::collections::BTreeMap;
|
||||
|
||||
/// Detect broken font encodings in extracted markdown text.
|
||||
///
|
||||
/// Two heuristics:
|
||||
/// 1. **U+FFFD**: Any replacement character indicates decode failures.
|
||||
/// 2. **Dollar-as-space**: Pattern like `Word$Word$Word` where `$` is used as a
|
||||
/// word separator due to broken ToUnicode CMaps. Triggers when either:
|
||||
/// - More than 50% of `$` are between letters (clear substitution pattern), OR
|
||||
/// - More than 20 letter-dollar-letter occurrences (even if some `$` are also
|
||||
/// used as trailing/leading separators, 20+ is far beyond normal financial text).
|
||||
pub(crate) fn detect_encoding_issues(markdown: &str) -> bool {
|
||||
// Heuristic 1: U+FFFD replacement characters
|
||||
if markdown.contains('\u{FFFD}') {
|
||||
return true;
|
||||
}
|
||||
|
||||
// Heuristic 2: dollar-as-space pattern
|
||||
if has_dollar_as_space_pattern(markdown) {
|
||||
return true;
|
||||
}
|
||||
|
||||
// Heuristic 3: substitution-cipher letter statistics (broken ToUnicode)
|
||||
let mut stats = CipherGarbleStats::default();
|
||||
stats.add_text(markdown);
|
||||
stats.looks_garbled()
|
||||
}
|
||||
|
||||
fn has_dollar_as_space_pattern(markdown: &str) -> bool {
|
||||
let total_dollars = markdown.matches('$').count();
|
||||
if total_dollars > 10 {
|
||||
let bytes = markdown.as_bytes();
|
||||
let mut letter_dollar_letter = 0usize;
|
||||
for i in 1..bytes.len().saturating_sub(1) {
|
||||
if bytes[i] == b'$'
|
||||
&& bytes[i - 1].is_ascii_alphabetic()
|
||||
&& bytes[i + 1].is_ascii_alphabetic()
|
||||
{
|
||||
letter_dollar_letter += 1;
|
||||
}
|
||||
}
|
||||
if letter_dollar_letter > 20 || letter_dollar_letter * 2 > total_dollars {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
false
|
||||
}
|
||||
|
||||
/// English letter frequencies (percent, a–z). Used as a natural-language
|
||||
/// reference: every Latin-script language in the eval corpus (Swedish,
|
||||
/// Finnish, Turkish, German, romaji) scores ≥ 0.80 cosine similarity against
|
||||
/// it, while substitution-cipher text scores ~0.53.
|
||||
const ENGLISH_LETTER_FREQ: [f64; 26] = [
|
||||
8.2, 1.5, 2.8, 4.3, 12.7, 2.2, 2.0, 6.1, 7.0, 0.15, 0.8, 4.0, 2.4, 6.7, 7.5, 1.9, 0.1, 6.0,
|
||||
6.3, 9.1, 2.8, 1.0, 2.4, 0.15, 2.0, 0.07,
|
||||
];
|
||||
|
||||
/// Letter statistics for detecting substitution-cipher garbling: broken
|
||||
/// ToUnicode CMaps that shift every character by a per-range constant (e.g.
|
||||
/// `Certificate` extracted as `8VceZWZTReV`). Such text is 100% printable
|
||||
/// ASCII with word-like token lengths, so it defeats `is_garbage_text` and
|
||||
/// produces no replacement characters — it needs its own discriminator.
|
||||
#[derive(Debug, Default)]
|
||||
struct CipherGarbleStats {
|
||||
/// Case-folded ASCII letter histogram.
|
||||
letter_counts: [u32; 26],
|
||||
ascii_letters: usize,
|
||||
ascii_vowels: usize,
|
||||
/// Accented Latin letters (Latin-1 Supplement through Latin Extended-B,
|
||||
/// plus Latin Extended Additional). Count toward Latin dominance only.
|
||||
latin_ext_letters: usize,
|
||||
non_latin_letters: usize,
|
||||
/// Adjacent ASCII-letter pairs, and how many of them switch from
|
||||
/// lowercase straight to uppercase mid-word.
|
||||
letter_bigrams: usize,
|
||||
case_shift_bigrams: usize,
|
||||
}
|
||||
|
||||
impl CipherGarbleStats {
|
||||
fn add_text(&mut self, text: &str) {
|
||||
let mut prev: Option<char> = None;
|
||||
for ch in text.chars() {
|
||||
if ch.is_ascii_alphabetic() {
|
||||
let idx = (ch.to_ascii_lowercase() as u8 - b'a') as usize;
|
||||
self.letter_counts[idx] += 1;
|
||||
self.ascii_letters += 1;
|
||||
if matches!(ch.to_ascii_lowercase(), 'a' | 'e' | 'i' | 'o' | 'u') {
|
||||
self.ascii_vowels += 1;
|
||||
}
|
||||
if let Some(p) = prev {
|
||||
self.letter_bigrams += 1;
|
||||
if p.is_ascii_lowercase() && ch.is_ascii_uppercase() {
|
||||
self.case_shift_bigrams += 1;
|
||||
}
|
||||
}
|
||||
prev = Some(ch);
|
||||
} else {
|
||||
if ch.is_alphabetic() {
|
||||
if matches!(ch as u32, 0xC0..=0x24F | 0x1E00..=0x1EFF) {
|
||||
self.latin_ext_letters += 1;
|
||||
} else {
|
||||
self.non_latin_letters += 1;
|
||||
}
|
||||
}
|
||||
prev = None;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Cosine similarity between the observed letter histogram and English
|
||||
/// letter frequencies. A shifted alphabet permutes the histogram, which
|
||||
/// destroys the similarity regardless of the shift amount.
|
||||
fn english_cosine(&self) -> f64 {
|
||||
if self.ascii_letters == 0 {
|
||||
return 1.0;
|
||||
}
|
||||
let n = self.ascii_letters as f64;
|
||||
let mut dot = 0.0;
|
||||
let mut norm_obs = 0.0;
|
||||
for (count, freq) in self.letter_counts.iter().zip(ENGLISH_LETTER_FREQ) {
|
||||
let p = *count as f64 / n;
|
||||
dot += p * freq;
|
||||
norm_obs += p * p;
|
||||
}
|
||||
let norm_en = ENGLISH_LETTER_FREQ
|
||||
.iter()
|
||||
.map(|f| f * f)
|
||||
.sum::<f64>()
|
||||
.sqrt();
|
||||
dot / (norm_obs.sqrt() * norm_en)
|
||||
}
|
||||
|
||||
/// Cosine similarity between the observed histogram and English
|
||||
/// frequencies after sorting BOTH descending — i.e. comparing the *shape*
|
||||
/// of the frequency profile, ignoring which letter sits where. A
|
||||
/// substitution cipher is a bijection, so it preserves this shape exactly
|
||||
/// (att10k 0.97, arbitrary shifts 0.99) regardless of case or offset.
|
||||
/// Non-linguistic ASCII has a different profile: a small alphabet is far
|
||||
/// steeper (random DNA 0.74, hex dumps 0.81), so the shape diverges.
|
||||
fn english_shape_cosine(&self) -> f64 {
|
||||
if self.ascii_letters == 0 {
|
||||
return 1.0;
|
||||
}
|
||||
let n = self.ascii_letters as f64;
|
||||
let mut obs: [f64; 26] = std::array::from_fn(|i| self.letter_counts[i] as f64 / n);
|
||||
obs.sort_unstable_by(|a, b| b.total_cmp(a));
|
||||
let mut en = ENGLISH_LETTER_FREQ;
|
||||
en.sort_unstable_by(|a, b| b.total_cmp(a));
|
||||
|
||||
let dot: f64 = obs.iter().zip(en).map(|(o, e)| o * e).sum();
|
||||
let norm_obs = obs.iter().map(|o| o * o).sum::<f64>().sqrt();
|
||||
let norm_en = en.iter().map(|e| e * e).sum::<f64>().sqrt();
|
||||
dot / (norm_obs * norm_en)
|
||||
}
|
||||
|
||||
/// Thresholds validated against the 380-document pdf-evals snapshot
|
||||
/// corpus (0 false positives) and the garbled ParseBench `att10k` page
|
||||
/// (vowel ratio 0.245, case-shift rate 0.225, cosine 0.532). Closest
|
||||
/// legitimate document on each axis: vowel ratio 0.264 (circuit
|
||||
/// schematic), case-shift rate 0.021, cosine 0.801.
|
||||
fn looks_garbled(&self) -> bool {
|
||||
// Need a statistically meaningful, Latin-dominant sample.
|
||||
if self.ascii_letters < 200
|
||||
|| self.non_latin_letters > self.ascii_letters + self.latin_ext_letters
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Real Latin-script text keeps vowels above ~30% of letters even in
|
||||
// acronym- and part-number-heavy documents; shifted text starves them.
|
||||
let vowel_ratio = self.ascii_vowels as f64 / self.ascii_letters as f64;
|
||||
if vowel_ratio > 0.30 {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Signal 1: lowercase→uppercase transitions inside words. A shifted
|
||||
// lowercase alphabet straddles the ASCII uppercase block ('i'→'Z',
|
||||
// 't'→'e'), so garbled words flip case constantly. Real documents
|
||||
// stay ≤ 0.02 even with camelCase identifiers.
|
||||
let case_shifts = self.letter_bigrams >= 100
|
||||
&& self.case_shift_bigrams as f64 >= self.letter_bigrams as f64 * 0.10;
|
||||
|
||||
// Signal 2: the histogram is a permutation of natural language — an
|
||||
// English-like frequency SHAPE (sorted cosine high) but with letters
|
||||
// in the wrong POSITIONS (unsorted cosine low). This is the signature
|
||||
// of a substitution cipher and is case-independent, so it catches
|
||||
// all-lowercase and all-uppercase shifts as well as case-straddling
|
||||
// ones. Genuinely non-linguistic ASCII that is merely "unlike English"
|
||||
// fails one of the two halves: DNA/hex dumps have too steep a profile
|
||||
// (shape cosine < 0.90), while protein sequences, ticker symbols and
|
||||
// base64 are not sufficiently unlike English in position (unsorted
|
||||
// cosine ≥ 0.60) — so none of them are routed to OCR.
|
||||
let permuted_language = self.english_cosine() < 0.60 && self.english_shape_cosine() >= 0.90;
|
||||
|
||||
case_shifts || permuted_language
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Default)]
|
||||
pub(crate) struct TextQualityReport {
|
||||
pub(crate) pages_needing_ocr: Vec<u32>,
|
||||
pub(crate) has_encoding_issues: bool,
|
||||
pub(crate) reasons_by_page: BTreeMap<u32, Vec<String>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Default)]
|
||||
struct PageTextQualityEvidence {
|
||||
chars: usize,
|
||||
replacement_chars: usize,
|
||||
replacement_spans: usize,
|
||||
longest_replacement_run: usize,
|
||||
cipher_garble: CipherGarbleStats,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
enum TextSpanIssueKind {
|
||||
Replacement,
|
||||
Strong,
|
||||
}
|
||||
|
||||
pub(crate) fn analyze_text_quality(items: &[TextItem]) -> TextQualityReport {
|
||||
let mut reasons_by_page = BTreeMap::new();
|
||||
let mut evidence_by_page = BTreeMap::<u32, PageTextQualityEvidence>::new();
|
||||
|
||||
for item in items {
|
||||
if !matches!(item.item_type, crate::types::ItemType::Text) {
|
||||
continue;
|
||||
}
|
||||
|
||||
let evidence = evidence_by_page.entry(item.page).or_default();
|
||||
evidence.chars += item.text.chars().filter(|ch| !ch.is_whitespace()).count();
|
||||
evidence.cipher_garble.add_text(&item.text);
|
||||
|
||||
match text_span_decoding_issue_kind(&item.text) {
|
||||
Some(TextSpanIssueKind::Strong) => {
|
||||
add_ocr_reason(
|
||||
&mut reasons_by_page,
|
||||
item.page,
|
||||
OCR_REASON_SUSPECTED_GARBLED_TEXT,
|
||||
);
|
||||
}
|
||||
Some(TextSpanIssueKind::Replacement) => {
|
||||
let stats = replacement_text_stats(&item.text);
|
||||
evidence.replacement_chars += stats.0;
|
||||
evidence.replacement_spans += 1;
|
||||
evidence.longest_replacement_run = evidence.longest_replacement_run.max(stats.1);
|
||||
}
|
||||
None => {}
|
||||
}
|
||||
}
|
||||
|
||||
for (page, evidence) in evidence_by_page {
|
||||
if reasons_by_page.contains_key(&page) {
|
||||
continue;
|
||||
}
|
||||
if page_replacement_evidence_needs_ocr(&evidence) || evidence.cipher_garble.looks_garbled()
|
||||
{
|
||||
add_ocr_reason(
|
||||
&mut reasons_by_page,
|
||||
page,
|
||||
OCR_REASON_SUSPECTED_GARBLED_TEXT,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
let pages_needing_ocr: Vec<u32> = reasons_by_page.keys().copied().collect();
|
||||
TextQualityReport {
|
||||
has_encoding_issues: !pages_needing_ocr.is_empty(),
|
||||
pages_needing_ocr,
|
||||
reasons_by_page,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn region_items_have_decoding_issue(items: &[TextItem]) -> bool {
|
||||
items.iter().any(|item| {
|
||||
matches!(item.item_type, crate::types::ItemType::Text)
|
||||
&& text_span_has_decoding_issue(&item.text)
|
||||
})
|
||||
}
|
||||
|
||||
fn text_span_has_decoding_issue(text: &str) -> bool {
|
||||
text_span_decoding_issue_kind(text).is_some()
|
||||
}
|
||||
|
||||
fn text_span_decoding_issue_kind(text: &str) -> Option<TextSpanIssueKind> {
|
||||
let text = text.trim();
|
||||
if text.is_empty() {
|
||||
return None;
|
||||
}
|
||||
|
||||
if has_dollar_as_space_pattern(text)
|
||||
|| has_private_use_text_run(text)
|
||||
|| is_cid_garbage(text)
|
||||
|| has_cid_control_token(text)
|
||||
{
|
||||
return Some(TextSpanIssueKind::Strong);
|
||||
}
|
||||
|
||||
if has_replacement_text_run(text) {
|
||||
return Some(TextSpanIssueKind::Replacement);
|
||||
}
|
||||
|
||||
None
|
||||
}
|
||||
|
||||
fn replacement_text_stats(text: &str) -> (usize, usize) {
|
||||
let mut replacement = 0usize;
|
||||
let mut current_run = 0usize;
|
||||
let mut longest_run = 0usize;
|
||||
|
||||
for ch in text.chars() {
|
||||
if ch == '\u{FFFD}' {
|
||||
replacement += 1;
|
||||
current_run += 1;
|
||||
longest_run = longest_run.max(current_run);
|
||||
} else {
|
||||
current_run = 0;
|
||||
}
|
||||
}
|
||||
|
||||
(replacement, longest_run)
|
||||
}
|
||||
|
||||
fn page_replacement_evidence_needs_ocr(evidence: &PageTextQualityEvidence) -> bool {
|
||||
if evidence.replacement_chars == 0 || evidence.chars == 0 {
|
||||
return false;
|
||||
}
|
||||
|
||||
// If the entire page is only a short broken text layer, even a short
|
||||
// replacement run is enough evidence. On otherwise text-heavy pages,
|
||||
// require density so math formulas do not force full-page OCR.
|
||||
if evidence.chars <= 80 && evidence.longest_replacement_run >= 2 {
|
||||
return true;
|
||||
}
|
||||
|
||||
let replacement_density_bps = evidence.replacement_chars * 10_000 / evidence.chars;
|
||||
let enough_bad_text = evidence.replacement_chars >= 12 && replacement_density_bps >= 500;
|
||||
let repeated_bad_spans = evidence.replacement_spans >= 3 && replacement_density_bps >= 250;
|
||||
let long_bad_run = evidence.longest_replacement_run >= 8 && replacement_density_bps >= 250;
|
||||
|
||||
enough_bad_text || repeated_bad_spans || long_bad_run
|
||||
}
|
||||
|
||||
fn has_replacement_text_run(text: &str) -> bool {
|
||||
let (replacement, longest_run) = replacement_text_stats(text);
|
||||
longest_run >= 2 || replacement >= 3
|
||||
}
|
||||
|
||||
fn has_private_use_text_run(text: &str) -> bool {
|
||||
let mut total = 0usize;
|
||||
let mut private_use = 0usize;
|
||||
let mut current_run = 0usize;
|
||||
let mut longest_run = 0usize;
|
||||
|
||||
for ch in text.chars() {
|
||||
if ch.is_whitespace() {
|
||||
current_run = 0;
|
||||
continue;
|
||||
}
|
||||
total += 1;
|
||||
if is_private_use_char(ch) {
|
||||
private_use += 1;
|
||||
current_run += 1;
|
||||
longest_run = longest_run.max(current_run);
|
||||
} else {
|
||||
current_run = 0;
|
||||
}
|
||||
}
|
||||
|
||||
if private_use == 0 {
|
||||
return false;
|
||||
}
|
||||
|
||||
longest_run >= 3 || (total >= 5 && private_use >= 2 && private_use * 2 >= total)
|
||||
}
|
||||
|
||||
fn has_cid_control_token(text: &str) -> bool {
|
||||
text.split_whitespace().any(token_has_cid_control)
|
||||
}
|
||||
|
||||
fn token_has_cid_control(token: &str) -> bool {
|
||||
let mut total = 0usize;
|
||||
let mut c1_control = 0usize;
|
||||
|
||||
for ch in token.chars() {
|
||||
total += 1;
|
||||
if ('\u{0080}'..='\u{009F}').contains(&ch) {
|
||||
c1_control += 1;
|
||||
}
|
||||
}
|
||||
|
||||
total >= 5 && c1_control >= 2 && c1_control * 20 >= total
|
||||
}
|
||||
|
||||
fn is_private_use_char(ch: char) -> bool {
|
||||
matches!(
|
||||
ch as u32,
|
||||
0xE000..=0xF8FF | 0xF0000..=0xFFFFD | 0x100000..=0x10FFFD
|
||||
)
|
||||
}
|
||||
|
||||
/// Check if extracted text is predominantly garbage (non-alphanumeric).
|
||||
///
|
||||
/// Broken font encodings produce text like "----1-.-.-.___ --.-. .._ I_---."
|
||||
/// where most characters are punctuation/symbols. Real text in any language
|
||||
/// has >50% alphanumeric characters.
|
||||
pub(crate) fn is_garbage_text(markdown: &str) -> bool {
|
||||
let mut alphanum = 0usize;
|
||||
let mut non_alphanum = 0usize;
|
||||
|
||||
let chars: Vec<char> = markdown.chars().collect();
|
||||
let mut i = 0usize;
|
||||
while i < chars.len() {
|
||||
let ch = chars[i];
|
||||
let mut run_end = i + 1;
|
||||
while run_end < chars.len() && chars[run_end] == ch {
|
||||
run_end += 1;
|
||||
}
|
||||
|
||||
let is_decorative_leader = matches!(ch, '.' | '_' | '·') && run_end - i >= 3;
|
||||
if !is_decorative_leader {
|
||||
for &run_ch in &chars[i..run_end] {
|
||||
if run_ch.is_whitespace() {
|
||||
continue;
|
||||
}
|
||||
// Skip markdown syntax chars that we add (not from the PDF)
|
||||
if matches!(run_ch, '#' | '*' | '|' | '-' | '\n') {
|
||||
continue;
|
||||
}
|
||||
if run_ch.is_alphanumeric() {
|
||||
alphanum += 1;
|
||||
} else {
|
||||
non_alphanum += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
i = run_end;
|
||||
}
|
||||
|
||||
let total = alphanum + non_alphanum;
|
||||
total >= 50 && alphanum * 2 < total
|
||||
}
|
||||
|
||||
/// Detect garbage from failed CID-to-Unicode mapping on Identity-H fonts.
|
||||
///
|
||||
/// When CID values don't correspond to Unicode codepoints, the raw bytes often
|
||||
/// produce characters in the C1 control range (U+0080–U+009F) or Private Use
|
||||
/// Area, mixed with random Latin Extended characters. Valid text in any
|
||||
/// language almost never contains C1 controls. We also fall back to the
|
||||
/// general `is_garbage_text` check for non-alphanumeric-heavy patterns.
|
||||
pub(crate) fn is_cid_garbage(text: &str) -> bool {
|
||||
if is_garbage_text(text) {
|
||||
return true;
|
||||
}
|
||||
let mut total = 0usize;
|
||||
let mut c1_control = 0usize;
|
||||
let mut high_latin = 0usize;
|
||||
for ch in text.chars() {
|
||||
if ch.is_whitespace() {
|
||||
continue;
|
||||
}
|
||||
total += 1;
|
||||
// C1 control characters (U+0080–U+009F) — almost never in real text
|
||||
if ch == '·' {
|
||||
continue;
|
||||
}
|
||||
if ('\u{0080}'..='\u{009F}').contains(&ch) {
|
||||
c1_control += 1;
|
||||
}
|
||||
// High Latin-1 (U+00A0–U+00FF) — legitimate in Western European text
|
||||
// but when combined with ASCII in CID passthrough, indicates mojibake
|
||||
// from CID values being misinterpreted as Latin-1 characters.
|
||||
if ('\u{00A0}'..='\u{00FF}').contains(&ch) {
|
||||
high_latin += 1;
|
||||
}
|
||||
}
|
||||
if total < 5 {
|
||||
return false;
|
||||
}
|
||||
// If ≥5% of non-whitespace chars are C1 controls, it's garbage
|
||||
if c1_control >= 2 && c1_control * 20 >= total {
|
||||
return true;
|
||||
}
|
||||
// If ≥40% of non-whitespace chars are high Latin-1 AND the text has few
|
||||
// ASCII letters, it's likely CID-as-Latin-1 mojibake (Japanese/CJK PDFs
|
||||
// where CID values 0x80-0xFF become accented Latin characters). Keep a
|
||||
// minimum length so short math tokens like "2×()×" do not route a clean
|
||||
// page to OCR.
|
||||
let ascii_letters = text.chars().filter(|c| c.is_ascii_alphabetic()).count();
|
||||
total >= 20 && high_latin * 5 >= total * 2 && ascii_letters * 3 < total
|
||||
}
|
||||
Reference in New Issue
Block a user