pdf_oxide 0.3.30

//! Non-text content detection for PDF extraction.
//!
//! This module detects whether character sequences represent non-text content
//! (such as embedded figures, diagrams, or other visual elements) rather than
//! actual text. This helps avoid extracting garbled characters from figures
//! that have high percentages of unmapped glyphs.
//!
//! # Phase 3: Enhanced ToUnicode Fallback
//!
//! Phase 3 improves extraction quality by:
//! 1. Detecting non-text content sequences
//! 2. Computing character mapping confidence scores
//! 3. Marking or skipping figures/diagrams in output
//! 4. Preserving text extraction accuracy

use crate::layout::TextSpan;

/// Confidence score for character-to-Unicode mapping (0.0 to 1.0).
///
/// Represents how confident we are that a given character code
/// maps to valid Unicode text rather than being garbage/diagram content.
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct CharacterConfidence {
    /// Overall confidence score (0.0 = certain garbage, 1.0 = certain text)
    pub score: f32,
    /// Reason for the confidence score
    pub reason: ConfidenceReason,
}

/// Reason why a character has a certain confidence score.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ConfidenceReason {
    /// Character has explicit ToUnicode mapping
    MappedByToUnicode,
    /// Character in standard encoding (ASCII, Latin-1, etc.)
    StandardEncoding,
    /// Character from font's built-in encoding
    FontEncoding,
    /// Fallback mapping using font name hints (Symbol, Wingdings, etc.)
    FontHintFallback,
    /// Unmapped character with no mapping available
    Unmapped,
    /// Character appears in suspicious context (likely diagram/figure)
    SuspiciousContext,
}

impl CharacterConfidence {
    /// Create a confidence score for a mapped character.
    pub fn mapped() -> Self {
        Self {
            score: 0.95,
            reason: ConfidenceReason::MappedByToUnicode,
        }
    }

    /// Create a confidence score for a standard encoding character.
    pub fn standard_encoding() -> Self {
        Self {
            score: 0.9,
            reason: ConfidenceReason::StandardEncoding,
        }
    }

    /// Create a confidence score for an unmapped character.
    pub fn unmapped() -> Self {
        Self {
            score: 0.3,
            reason: ConfidenceReason::Unmapped,
        }
    }

    /// Create a confidence score for a suspicious context.
    pub fn suspicious(score: f32) -> Self {
        Self {
            score: score.clamp(0.0, 1.0),
            reason: ConfidenceReason::SuspiciousContext,
        }
    }
}

/// Statistics for non-text content detection.
#[derive(Debug, Clone, Default)]
pub struct NonTextStats {
    /// Total characters analyzed
    pub total_chars: usize,
    /// Number of mapped characters
    pub mapped_chars: usize,
    /// Number of unmapped characters
    pub unmapped_chars: usize,
    /// Average confidence score
    pub avg_confidence: f32,
    /// Percentage of unmapped characters (0.0 to 1.0)
    pub unmapped_ratio: f32,
    /// Likely non-text content flag
    pub likely_non_text: bool,
}

/// Detector for non-text content in character sequences.
#[derive(Debug, Clone)]
pub struct NonTextDetector {
    /// Threshold for unmapped ratio to classify as non-text (default: 0.5)
    pub unmapped_threshold: f32,
    /// Threshold for confidence score to classify as non-text (default: 0.4)
    pub confidence_threshold: f32,
    /// Minimum sequence length to evaluate
    pub min_sequence_length: usize,
}

impl Default for NonTextDetector {
    fn default() -> Self {
        Self {
            unmapped_threshold: 0.5,   // >50% unmapped = likely figure
            confidence_threshold: 0.4, // avg confidence <0.4 = likely figure
            min_sequence_length: 10,
        }
    }
}

impl NonTextDetector {
    /// Create a new non-text detector with default settings.
    pub fn new() -> Self {
        Self::default()
    }

    /// Analyze a character sequence for non-text content indicators.
    ///
    /// # Arguments
    ///
    /// * `text` - The extracted text (may contain unmapped characters)
    /// * `confidences` - Per-character confidence scores
    /// * `font_name` - Name of the font (for heuristics)
    ///
    /// # Returns
    ///
    /// Statistics about the sequence and whether it's likely non-text content.
    pub fn analyze_sequence(
        &self,
        text: &str,
        confidences: &[CharacterConfidence],
        font_name: &str,
    ) -> NonTextStats {
        if text.len() < self.min_sequence_length {
            return NonTextStats::default();
        }

        let total_chars = text.len();
        let mapped_chars = confidences
            .iter()
            .filter(|c| c.reason != ConfidenceReason::Unmapped)
            .count();
        let unmapped_chars = total_chars - mapped_chars;
        let unmapped_ratio = unmapped_chars as f32 / total_chars as f32;

        let avg_confidence = if !confidences.is_empty() {
            confidences.iter().map(|c| c.score).sum::<f32>() / confidences.len() as f32
        } else {
            0.0
        };

        // Classify as likely non-text if:
        // 1. High unmapped ratio (>50%)
        // 2. Low average confidence (<0.4)
        // 3. Font name suggests symbol/diagram font (Symbol, Wingdings, etc.)
        let likely_non_text = unmapped_ratio > self.unmapped_threshold
            || avg_confidence < self.confidence_threshold
            || self.is_diagram_font(font_name);

        NonTextStats {
            total_chars,
            mapped_chars,
            unmapped_chars,
            avg_confidence,
            unmapped_ratio,
            likely_non_text,
        }
    }

    /// Check if a font name suggests diagram/symbol content.
    fn is_diagram_font(&self, font_name: &str) -> bool {
        let name_lower = font_name.to_lowercase();
        [
            "symbol",
            "wingdings",
            "webdings",
            "zapf dingbats",
            "dingbats",
            "mathematical alphanumeric",
        ]
        .iter()
        .any(|&pattern| name_lower.contains(pattern))
    }

    /// Detect and mark sequences as non-text content.
    ///
    /// This method analyzes spans and marks those that likely represent
    /// figures, diagrams, or other non-text content.
    pub fn mark_non_text_spans(&self, spans: &[TextSpan]) -> Vec<SpanClassification> {
        spans
            .iter()
            .enumerate()
            .map(|(idx, span)| {
                // For now, use a simple heuristic:
                // If span has mostly non-ASCII characters or low-confidence mappings,
                // it's likely non-text content
                let non_ascii_ratio = span.text.chars().filter(|c| !c.is_ascii()).count() as f32
                    / span.text.len().max(1) as f32;

                let is_likely_non_text = non_ascii_ratio > 0.3 || // >30% non-ASCII
                    has_suspicious_patterns(&span.text);

                SpanClassification {
                    span_index: idx,
                    span: span.clone(),
                    is_non_text: is_likely_non_text,
                    confidence: if is_likely_non_text { 0.6 } else { 0.9 },
                }
            })
            .collect()
    }
}

/// Classification of a text span.
#[derive(Debug, Clone)]
pub struct SpanClassification {
    /// Index of the span in original array
    pub span_index: usize,
    /// The text span itself
    pub span: TextSpan,
    /// Whether this span likely contains non-text content
    pub is_non_text: bool,
    /// Confidence in the classification (0.0 to 1.0)
    pub confidence: f32,
}

/// Check if text contains suspicious patterns indicating non-text content.
fn has_suspicious_patterns(text: &str) -> bool {
    // Patterns that suggest diagram/figure content:
    // 1. Many consecutive special Unicode characters
    // 2. Mix of widely disparate Unicode blocks
    // 3. Very short text with many non-ASCII chars

    let special_char_count = text
        .chars()
        .filter(|c| {
            let code = *c as u32;
            // Ranges known to contain diagram/symbol glyphs
            matches!(
                code,
                0x2600..=0x27BF |   // Miscellaneous Symbols and Dingbats
                0x1F300..=0x1F9FF | // Emoticons and pictographs
                0x2200..=0x22FF |   // Mathematical Operators
                0x2A00..=0x2AFF |   // Supplemental Mathematical Operators
                0x0080..=0x009F     // C1 Control Codes (often unmapped)
            )
        })
        .count();

    let special_ratio = special_char_count as f32 / text.len().max(1) as f32;

    // If >40% of characters are from special Unicode blocks, likely diagram
    special_ratio > 0.4
}

/// Compute mapping confidence for a character sequence.
///
/// Analyzes how many characters in a sequence have valid Unicode mappings
/// versus how many are unmapped or garbled.
pub fn compute_sequence_confidence(
    text: &str,
    mapped_count: usize,
    font_name: &str,
) -> CharacterConfidence {
    if text.is_empty() {
        return CharacterConfidence::unmapped();
    }

    let total = text.len();
    let mapped_ratio = mapped_count as f32 / total as f32;

    // Adjust score based on mapping quality
    let score: f32 = if mapped_ratio > 0.9 {
        // >90% mapped: likely good text
        0.85
    } else if mapped_ratio > 0.75 {
        // 75-90% mapped: probably text with some foreign chars
        0.7
    } else if mapped_ratio > 0.5 {
        // 50-75% mapped: mixed quality
        0.5
    } else {
        // <50% mapped: likely diagram/garbage
        0.2
    };

    CharacterConfidence {
        score: score.clamp(0.0_f32, 1.0_f32),
        reason: if is_likely_diagram_font(font_name) {
            ConfidenceReason::SuspiciousContext
        } else {
            ConfidenceReason::Unmapped
        },
    }
}

/// Check if a font name suggests symbol/diagram content.
fn is_likely_diagram_font(font_name: &str) -> bool {
    let name_lower = font_name.to_lowercase();
    name_lower.contains("symbol")
        || name_lower.contains("wingdings")
        || name_lower.contains("webdings")
        || name_lower.contains("dingbats")
}

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

    #[test]
    fn test_character_confidence_mapped() {
        let conf = CharacterConfidence::mapped();
        assert_eq!(conf.reason, ConfidenceReason::MappedByToUnicode);
        assert!(conf.score > 0.9);
    }

    #[test]
    fn test_character_confidence_unmapped() {
        let conf = CharacterConfidence::unmapped();
        assert_eq!(conf.reason, ConfidenceReason::Unmapped);
        assert!(conf.score < 0.5);
    }

    #[test]
    fn test_non_text_detector_high_unmapped_ratio() {
        let detector = NonTextDetector::default();

        // Create mock confidences with high unmapped ratio
        let confidences = vec![
            CharacterConfidence::unmapped(),
            CharacterConfidence::unmapped(),
            CharacterConfidence::unmapped(),
            CharacterConfidence::mapped(),
            CharacterConfidence::mapped(),
            CharacterConfidence::unmapped(),
            CharacterConfidence::unmapped(),
            CharacterConfidence::unmapped(),
            CharacterConfidence::unmapped(),
            CharacterConfidence::unmapped(),
        ];

        let stats = detector.analyze_sequence("äöüäöüäöüX", &confidences, "Helvetica");
        assert!(stats.likely_non_text); // >50% unmapped
    }

    #[test]
    fn test_non_text_detector_symbol_font() {
        let detector = NonTextDetector::default();
        let confidences = vec![CharacterConfidence::mapped(); 10];

        // Symbol fonts should be flagged even with good confidence
        let stats = detector.analyze_sequence("test content 123", &confidences, "Symbol");
        assert!(stats.likely_non_text);
    }

    #[test]
    fn test_non_text_detector_normal_text() {
        let detector = NonTextDetector::default();
        let confidences = vec![CharacterConfidence::mapped(); 10];

        let stats = detector.analyze_sequence("hello world test", &confidences, "Arial");
        assert!(!stats.likely_non_text);
    }

    #[test]
    fn test_suspicious_patterns() {
        // Normal text
        assert!(!has_suspicious_patterns("The quick brown fox"));

        // Text with some accents is OK
        assert!(!has_suspicious_patterns("Café résumé naïve"));
    }

    #[test]
    fn test_sequence_confidence_high_mapped() {
        let conf = compute_sequence_confidence("Hello World", 11, "Arial");
        assert!(conf.score > 0.7);
    }

    #[test]
    fn test_sequence_confidence_low_mapped() {
        let conf = compute_sequence_confidence("☺♦♠♥♣", 1, "Arial");
        assert!(conf.score < 0.5);
    }
}