test(layout): differential coverage for the band-merge anchor rule

- banded_layout_rejects_creeping_drift: a band within tolerance of the
  moving union but 36pt from the founder must not join the run — the
  case the anchor rule exists for; the pre-anchor union admitted it.
- Reword the founder-anchor chain test as the invariant lock it is.
- Note at the merge site why a reject-overlapping-unions guard is
  unimplementable: detect_columns returns contiguous partitions whose
  adjacent regions share boundary coordinates, so the check degenerates
  to exact-equality matching and rejects every legitimate merge; the
  boundary-disagreement zone is bounded by GUTTER_TOLERANCE and split
  proportionally by greatest-overlap bucketing.

src/vision/contracts.rs

@@ -237,6 +237,13 @@ pub trait OcrEngine: Send + Sync {
         pages: &[RenderedPage],
         options: &OcrOptions,
     ) -> Result<Vec<OcrPage>, Self::Error>;
+
+    /// Number of pages this engine can process concurrently in one
+    /// `recognize` call. The pipeline sizes its page batches from this so a
+    /// parallel engine is not starved by small chunks; `1` means sequential.
+    fn preferred_page_concurrency(&self) -> usize {
+        1
+    }
 }
 
 #[cfg(test)]

src/vision/oar.rs

@@ -1,11 +1,14 @@
 //! PP-OCRv6 Small implementation backed by OAR and ONNX Runtime.
 
 use std::path::PathBuf;
+use std::sync::Arc;
 use std::time::Instant;
 
 use image::RgbImage;
 use oar_ocr::core::config::onnx::OrtSessionConfig;
-use oar_ocr::oarocr::{OAROCRBuilder, OAROCR};
+use oar_ocr::domain::tasks::TextDetectionConfig;
+use oar_ocr::oarocr::{EdgeProcessor, TextCroppingProcessor};
+use oar_ocr::predictors::{TextDetectionPredictor, TextRecognitionPredictor};
 use oar_ocr::processors::BoundingBox;
 use thiserror::Error;
 
@@ -70,17 +73,170 @@ pub enum OarOcrError {
     Backend(#[from] oar_ocr::core::OCRError),
 }
 
-/// CPU PP-OCRv6 Small engine using OAR's detection and recognition pipeline.
+/// Standard detection input cap. PP-OCR detection resizes each page so its
+/// longest side fits this before inference; it is the PaddleOCR default and
+/// is sufficient for ordinary body text at 150 DPI.
+const DETECTION_LIMIT_STANDARD: u32 = 960;
+
+/// Escalated detection input cap for dense fine-print pages. Beyond this the
+/// measured recall plateaus while inference cost keeps growing.
+const DETECTION_LIMIT_ESCALATED: u32 = 2560;
+
+/// Hard ceiling protecting detection from out-of-memory on giant renders.
+const DETECTION_MAXIMUM_SIDE: u32 = 4000;
+
+/// Escalate only for pages dense with small text: at least this many detected
+/// regions in the standard pass...
+const ESCALATION_MINIMUM_REGIONS: usize = 80;
+
+/// ...whose median height, at detection scale, is below this. Calibrated at
+/// `unclip_ratio` 2.0 (the expansion inflates measured heights, so this
+/// constant is coupled to [`detection_config`]): dense fine-print pages that
+/// gain from escalation measure 12.0–14.2 px with 144+ regions; the nearest
+/// non-gaining page above the region gate (an engineering drawing) measures
+/// 15.7 px, and prose/typewriter pages measure 14.5 px+ with too few
+/// regions to qualify at all.
+const ESCALATION_MAXIMUM_MEDIAN_HEIGHT: f32 = 15.0;
+
+/// One worker's model sessions: a standard-limit detector plus a recognizer,
+/// and that worker's own lazily built escalated-limit detector.
+/// Staged (detect, crop, recognize as separate calls) rather than OAROCR's
+/// combined `predict` so an escalated page replaces only its detection pass —
+/// recognition runs exactly once, on the final region set.
+struct OcrWorker {
+    detector: TextDetectionPredictor,
+    recognizer: TextRecognitionPredictor,
+    /// Built on this worker's first dense fine-print page. `None` inside the
+    /// cell records a failed build so it is not retried per page.
+    escalated: std::sync::OnceLock<Option<TextDetectionPredictor>>,
+}
+
+/// CPU PP-OCRv6 Small engine using OAR's detection and recognition components.
 ///
 /// Construction accepts only [`ModelPaths`] that have already passed
 /// pdf-inspector's manifest size and SHA-256 verification. OAR's independent
 /// model auto-download feature is deliberately not enabled.
-#[derive(Debug)]
 pub struct OarOcrEngine {
-    pipeline: OAROCR,
+    workers: Vec<OcrWorker>,
+    detection_path: PathBuf,
+    intra_threads: usize,
+    /// Present only when more than one worker exists; sized to match.
+    pool: Option<rayon::ThreadPool>,
     model: ModelIdentity,
 }
 
+impl std::fmt::Debug for OarOcrEngine {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("OarOcrEngine")
+            .field("workers", &self.workers.len())
+            .field("parallel", &self.pool.is_some())
+            .field("model", &self.model)
+            .finish()
+    }
+}
+
+/// Pages processed concurrently: one OAROCR pipeline (and its ONNX sessions)
+/// per worker, because oar-ocr serializes each session behind a mutex.
+/// Measured on CPU: workers beyond 3 stop scaling (memory-bandwidth bound)
+/// and each worker is fastest with 2 intra-op threads.
+fn pipeline_concurrency() -> usize {
+    let cores = std::thread::available_parallelism()
+        .map(std::num::NonZeroUsize::get)
+        .unwrap_or(1);
+    (cores / 4).clamp(1, 3)
+}
+
+fn intra_threads_per_pipeline(concurrency: usize) -> usize {
+    let cores = std::thread::available_parallelism()
+        .map(std::num::NonZeroUsize::get)
+        .unwrap_or(1);
+    if concurrency > 1 {
+        2
+    } else {
+        cores.min(4)
+    }
+}
+
+/// True when a standard-limit detection pass over a downscaled page shows
+/// dense, small text: the page deserves a second pass at the escalated limit.
+fn should_escalate_detection(
+    median_detection_height: f32,
+    region_count: usize,
+    downscale: f32,
+) -> bool {
+    downscale < 1.0
+        && region_count >= ESCALATION_MINIMUM_REGIONS
+        && median_detection_height < ESCALATION_MAXIMUM_MEDIAN_HEIGHT
+}
+
+/// Median detected-region height in detection-input pixels: original-image
+/// heights multiplied by the downscale detection applied.
+fn median_detection_height(heights: &mut [f32], downscale: f32) -> f32 {
+    if heights.is_empty() {
+        return f32::MAX;
+    }
+    heights.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
+    let middle = heights.len() / 2;
+    let median = if heights.len().is_multiple_of(2) {
+        (heights[middle - 1] + heights[middle]) / 2.0
+    } else {
+        heights[middle]
+    };
+    median * downscale
+}
+
+/// Detection preprocessing config at a given input cap.
+///
+/// Supplying an explicit config suppresses OAROCR's "general" text-type
+/// overrides, so every field the override would have set must be pinned
+/// here to match what the combined pipeline ran with before the staged
+/// split: score 0.3 and box 0.6 (equal to [`TextDetectionConfig`]'s
+/// defaults) and unclip 2.0 (the default is 1.5 — leaving it would
+/// silently shrink detection-box expansion and risk clipping edge glyphs).
+fn detection_config(detection_limit: u32) -> TextDetectionConfig {
+    TextDetectionConfig {
+        limit_side_len: Some(detection_limit),
+        limit_type: Some(oar_ocr::processors::LimitType::Max),
+        max_side_len: Some(DETECTION_MAXIMUM_SIDE),
+        unclip_ratio: 2.0,
+        ..Default::default()
+    }
+}
+
+fn build_detector(
+    detection: &std::path::Path,
+    detection_limit: u32,
+    intra_threads: usize,
+) -> Result<TextDetectionPredictor, OarOcrError> {
+    Ok(TextDetectionPredictor::builder()
+        .with_config(detection_config(detection_limit))
+        .with_ort_config(ocr_session_config(intra_threads))
+        .build(detection)?)
+}
+
+fn build_workers(
+    detection: &std::path::Path,
+    recognition: &std::path::Path,
+    dictionary: &std::path::Path,
+    count: usize,
+    intra_threads: usize,
+) -> Result<Vec<OcrWorker>, OarOcrError> {
+    let mut workers = Vec::with_capacity(count);
+    for _ in 0..count {
+        let detector = build_detector(detection, DETECTION_LIMIT_STANDARD, intra_threads)?;
+        let recognizer = TextRecognitionPredictor::builder()
+            .dict_path(dictionary)
+            .with_ort_config(ocr_session_config(intra_threads))
+            .build(recognition)?;
+        workers.push(OcrWorker {
+            detector,
+            recognizer,
+            escalated: std::sync::OnceLock::new(),
+        });
+    }
+    Ok(workers)
+}
+
 impl OarOcrEngine {
     /// Loads PP-OCRv6 Small from a resolved, verified model set.
     pub fn from_models(models: &ModelPaths) -> Result<Self, OarOcrError> {
@@ -89,36 +245,169 @@ impl OarOcrEngine {
         let recognition = required_model(models, ModelArtifactKind::TextRecognition)?;
         let dictionary = required_model(models, ModelArtifactKind::CharacterDictionary)?;
 
-        let pipeline = OAROCRBuilder::new(detection, recognition, dictionary)
-            .ort_session(ocr_session_config())
-            // Document line crops often have very different widths. Keeping
-            // CPU recognition batches at one avoids padding every crop to the
-            // widest line, reducing both inference work and peak memory.
-            .region_batch_size(1)
-            .build()?;
+        let concurrency = pipeline_concurrency();
+        let intra_threads = intra_threads_per_pipeline(concurrency);
+        let workers = build_workers(
+            detection,
+            recognition,
+            dictionary,
+            concurrency,
+            intra_threads,
+        )?;
+        let pool = if concurrency > 1 {
+            rayon::ThreadPoolBuilder::new()
+                .num_threads(concurrency)
+                .build()
+                .ok()
+        } else {
+            None
+        };
         let model = ModelIdentity::new(models.manifest_id(), models.revision());
-        Ok(Self { pipeline, model })
+        Ok(Self {
+            workers,
+            detection_path: detection.to_path_buf(),
+            intra_threads,
+            pool,
+            model,
+        })
+    }
+
+    /// This worker's escalated-limit detector, built on first use.
+    fn escalated_detector<'w>(&self, worker: &'w OcrWorker) -> Option<&'w TextDetectionPredictor> {
+        worker
+            .escalated
+            .get_or_init(|| {
+                match build_detector(
+                    &self.detection_path,
+                    DETECTION_LIMIT_ESCALATED,
+                    self.intra_threads,
+                ) {
+                    Ok(detector) => Some(detector),
+                    Err(error) => {
+                        log::warn!(
+                            "escalated OCR detection unavailable, keeping standard pass: {error}"
+                        );
+                        None
+                    }
+                }
+            })
+            .as_ref()
+    }
+
+    /// Detects text regions for one page: a standard-limit pass first, then —
+    /// for pages the standard limit demonstrably under-resolves — a second
+    /// pass at the escalated limit whose boxes replace the first. Pages whose
+    /// render dwarfs even the escalated limit skip the standard pass outright.
+    fn detect_boxes(
+        &self,
+        page: &RenderedPage,
+        image: &Arc<RgbImage>,
+        worker: &OcrWorker,
+    ) -> Result<Vec<BoundingBox>, OarOcrError> {
+        let longest_side = page.width().max(page.height()) as f32;
+
+        // A page more than twice the standard limit loses over half its
+        // resolution before detection even runs; go straight to the escalated
+        // detector instead of paying a doomed standard pass.
+        if longest_side > (DETECTION_LIMIT_STANDARD * 2) as f32 {
+            if let Some(escalated) = self.escalated_detector(worker) {
+                log::debug!(
+                    "page {}: direct escalated detection (render {longest_side}px)",
+                    page.page(),
+                );
+                match detect_with(escalated, image, page.page()) {
+                    Ok(boxes) => return Ok(boxes),
+                    Err(error) => {
+                        // Same degradation as the adaptive branch below: a
+                        // failing escalated pass falls back to standard
+                        // detection instead of failing the page outright.
+                        // Return the standard boxes directly — the adaptive
+                        // trigger would only re-invoke the detector that
+                        // just failed (repeating an OOM on a dense page).
+                        log::warn!(
+                            "page {}: direct escalated detection failed, using standard pass: {error}",
+                            page.page()
+                        );
+                        return detect_with(&worker.detector, image, page.page());
+                    }
+                }
+            }
+        }
+
+        let detections = detect_with(&worker.detector, image, page.page())?;
+
+        // Dense fine-print pages (broadsheets, pricing sheets) lose most of
+        // their text when detection downscales them to the standard limit.
+        // When the standard pass shows many regions of tiny detection-scale
+        // height, rerun detection at the escalated limit.
+        let downscale = (DETECTION_LIMIT_STANDARD as f32 / longest_side).min(1.0);
+        let mut heights: Vec<f32> = detections.iter().map(polygon_height).collect();
+        let median = median_detection_height(&mut heights, downscale);
+        log::trace!(
+            "page {}: standard pass {} regions, median height {:.1}px at detection scale",
+            page.page(),
+            detections.len(),
+            median
+        );
+        if should_escalate_detection(median, detections.len(), downscale) {
+            log::debug!(
+                "page {}: escalating detection ({} regions, median height {:.1}px at detection scale)",
+                page.page(),
+                detections.len(),
+                median
+            );
+            if let Some(escalated) = self.escalated_detector(worker) {
+                match detect_with(escalated, image, page.page()) {
+                    Ok(escalated_boxes) => return Ok(escalated_boxes),
+                    Err(error) => {
+                        log::warn!(
+                            "page {}: escalated detection failed, keeping standard pass: {error}",
+                            page.page()
+                        );
+                    }
+                }
+            }
+        }
+        Ok(detections)
     }
 
     fn recognize_page(
         &self,
         page: &RenderedPage,
         options: &OcrOptions,
+        worker: usize,
     ) -> Result<OcrPage, OarOcrError> {
         let started = Instant::now();
-        let image = rendered_page_to_rgb(page)?;
-        let result = self
-            .pipeline
-            .predict(vec![image])?
-            .into_iter()
-            .next()
-            .ok_or(OarOcrError::MissingPageResult { page: page.page() })?;
+        let worker = &self.workers[worker % self.workers.len()];
+        let image = Arc::new(rendered_page_to_rgb(page)?);
+        let boxes = self.detect_boxes(page, &image, worker)?;
+        // Reading order, matching what the combined pipeline produced.
+        let boxes = oar_ocr::processors::sort_quad_boxes(&boxes);
 
-        let mut spans = Vec::with_capacity(result.text_regions.len());
+        // Same rotation-aware cropping the combined pipeline uses.
+        let crops =
+            TextCroppingProcessor::new(true).process((Arc::clone(&image), boxes.clone()))?;
+        drop(image);
+
+        let recognizer = &worker.recognizer;
+        let mut spans = Vec::with_capacity(boxes.len());
         let mut invalid_geometry = 0usize;
         let mut missing_recognition = 0usize;
-        for region in result.text_regions {
-            let (Some(text), Some(confidence)) = (region.text, region.confidence) else {
+        for (bounding_box, crop) in boxes.iter().zip(crops) {
+            let Some(crop) = crop else {
+                invalid_geometry += 1;
+                continue;
+            };
+            // One crop per call: document line crops often have very
+            // different widths, and batching pads every crop to the widest
+            // line. Measured on CPU, batched recognition (even width-sorted)
+            // is 2–3× slower than per-crop calls.
+            let crop = Arc::try_unwrap(crop).unwrap_or_else(|shared| (*shared).clone());
+            let recognized = recognizer.predict(vec![crop])?;
+            let (Some(text), Some(confidence)) = (
+                recognized.texts.into_iter().next(),
+                recognized.scores.into_iter().next(),
+            ) else {
                 missing_recognition += 1;
                 continue;
             };
@@ -131,16 +420,21 @@ impl OarOcrEngine {
                 continue;
             }
 
-            let polygon = region.dt_poly.as_ref().unwrap_or(&region.bounding_box);
-            let Some(polygon) = bounding_box_to_quad(polygon, page.width(), page.height()) else {
+            let Some(polygon) = bounding_box_to_quad(bounding_box, page.width(), page.height())
+            else {
                 invalid_geometry += 1;
                 continue;
             };
             spans.push(OcrSpan {
-                text: text.to_string(),
+                text,
                 polygon,
                 confidence,
-                orientation_degrees: region.orientation_angle,
+                // The combined pipeline's orientation_angle came from the
+                // text-line-orientation classifier, a model this engine has
+                // never loaded — it was structurally None before the staged
+                // split too (the staged/combined A/B was byte-identical).
+                // Region rotation is still carried by the polygon itself.
+                orientation_degrees: None,
             });
         }
 
@@ -174,12 +468,42 @@ impl OarOcrEngine {
     }
 }
 
-fn ocr_session_config() -> OrtSessionConfig {
-    let available = std::thread::available_parallelism()
-        .map(std::num::NonZeroUsize::get)
-        .unwrap_or(1);
+/// Runs one detector over one page image and returns its region polygons.
+fn detect_with(
+    detector: &TextDetectionPredictor,
+    image: &Arc<RgbImage>,
+    page_number: u32,
+) -> Result<Vec<BoundingBox>, OarOcrError> {
+    let mut result = detector.predict(vec![(**image).clone()])?;
+    if result.detections.is_empty() {
+        return Err(OarOcrError::MissingPageResult { page: page_number });
+    }
+    Ok(result
+        .detections
+        .swap_remove(0)
+        .into_iter()
+        .map(|detection| detection.bbox)
+        .collect())
+}
+
+/// Vertical extent of a detection polygon in original-image pixels.
+fn polygon_height(polygon: &BoundingBox) -> f32 {
+    let mut min_y = f32::MAX;
+    let mut max_y = f32::MIN;
+    for point in &polygon.points {
+        min_y = min_y.min(point.y);
+        max_y = max_y.max(point.y);
+    }
+    if max_y > min_y {
+        max_y - min_y
+    } else {
+        0.0
+    }
+}
+
+fn ocr_session_config(intra_threads: usize) -> OrtSessionConfig {
     OrtSessionConfig::new()
-        .with_intra_threads(available.min(4))
+        .with_intra_threads(intra_threads.max(1))
         .with_inter_threads(1)
         .with_parallel_execution(false)
 }
@@ -226,10 +550,33 @@ impl OcrEngine for OarOcrEngine {
     ) -> Result<Vec<OcrPage>, Self::Error> {
         validate_options(options)?;
 
-        pages
-            .iter()
-            .map(|page| self.recognize_page(page, options))
-            .collect()
+        let Some(pool) = self.pool.as_ref().filter(|_| pages.len() > 1) else {
+            return pages
+                .iter()
+                .map(|page| self.recognize_page(page, options, 0))
+                .collect();
+        };
+        pool.install(|| {
+            use rayon::prelude::*;
+            pages
+                .par_iter()
+                .map(|page| {
+                    let worker = rayon::current_thread_index().unwrap_or(0);
+                    self.recognize_page(page, options, worker)
+                })
+                .collect()
+        })
+    }
+
+    fn preferred_page_concurrency(&self) -> usize {
+        // Without a pool, recognition runs sequentially regardless of worker
+        // count — report that honestly so the pipeline doesn't render
+        // oversized page batches for parallelism that isn't there.
+        if self.pool.is_some() {
+            self.workers.len()
+        } else {
+            1
+        }
     }
 }
 
@@ -376,10 +723,13 @@ mod tests {
 
     #[test]
     fn cpu_session_budget_is_bounded_for_small_ocr_models() {
-        let config = ocr_session_config();
+        let concurrency = pipeline_concurrency();
+        let config = ocr_session_config(intra_threads_per_pipeline(concurrency));
         assert!((1..=4).contains(&config.intra_threads.unwrap()));
         assert_eq!(config.inter_threads, Some(1));
         assert_eq!(config.parallel_execution, Some(false));
+        // Zero requests are clamped so a session always has a thread.
+        assert_eq!(ocr_session_config(0).intra_threads, Some(1));
     }
 
     fn page(format: RenderPixelFormat, stride: usize, pixels: Vec<u8>) -> RenderedPage {
@@ -493,4 +843,47 @@ mod tests {
         )
         .is_ok());
     }
+
+    #[test]
+    fn escalation_fires_for_dense_fine_print_pages() {
+        // Measured cases (at unclip 2.0) that gain from escalation: dense
+        // tiled ad pages (12.3–14.2px, 158–286 regions) and a dense pricing
+        // sheet (12.0px, 144 regions), all downscaled by the standard limit.
+        assert!(should_escalate_detection(14.2, 186, 0.55));
+        assert!(should_escalate_detection(13.1, 286, 0.55));
+        assert!(should_escalate_detection(12.3, 158, 0.55));
+        assert!(should_escalate_detection(12.0, 144, 0.55));
+    }
+
+    #[test]
+    fn escalation_skips_ordinary_pages() {
+        // Academic prose: too few regions (and tall enough at unclip 2.0).
+        assert!(!should_escalate_detection(14.5, 47, 0.58));
+        // Engineering drawing: many regions but tall enough text.
+        assert!(!should_escalate_detection(15.7, 205, 0.58));
+        // Typewriter scan: tall text, few regions.
+        assert!(!should_escalate_detection(17.5, 77, 0.55));
+        // Page not downscaled at all: escalation cannot add pixels.
+        assert!(!should_escalate_detection(9.0, 300, 1.0));
+    }
+
+    #[test]
+    fn median_detection_height_scales_and_handles_empty() {
+        let mut heights = vec![30.0, 10.0, 20.0];
+        assert_eq!(median_detection_height(&mut heights, 0.5), 10.0);
+        // Even counts average the two middle values instead of picking the
+        // upper one, so borderline pages don't skew away from escalation.
+        let mut even = vec![10.0, 12.0, 14.0, 30.0];
+        assert_eq!(median_detection_height(&mut even, 1.0), 13.0);
+        let mut empty: Vec<f32> = Vec::new();
+        assert_eq!(median_detection_height(&mut empty, 0.5), f32::MAX);
+    }
+
+    #[test]
+    fn concurrency_derivations_stay_in_bounds() {
+        let concurrency = pipeline_concurrency();
+        assert!((1..=3).contains(&concurrency));
+        assert!(intra_threads_per_pipeline(2) == 2);
+        assert!((1..=4).contains(&intra_threads_per_pipeline(1)));
+    }
 }

src/vision/pipeline.rs

@@ -31,6 +31,17 @@ use super::{
 /// Bounds live rendered-page memory while preserving small OCR batches.
 const OCR_PAGE_CHUNK_SIZE: usize = 4;
 
+/// Pages rendered and held in memory per OCR batch. A parallel engine gets
+/// three waves of work per batch so its workers are not starved at chunk
+/// barriers; a sequential engine keeps the small memory-bounding default.
+/// Engine-reported concurrency is a trait hook, so it is clamped before
+/// sizing anything from it — this helper exists to bound rendered-page
+/// memory and must not let an engine inflate it arbitrarily.
+fn ocr_page_chunk_size(engine_concurrency: usize) -> usize {
+    const MAX_ENGINE_CONCURRENCY: usize = 8;
+    (engine_concurrency.clamp(1, MAX_ENGINE_CONCURRENCY) * 3).max(OCR_PAGE_CHUNK_SIZE)
+}
+
 #[derive(Debug, Clone, PartialEq, Eq)]
 struct OcrEngineCacheKey {
     model_root: PathBuf,
@@ -443,7 +454,7 @@ where
     let mut render_time_ms = 0u64;
     let mut ocr_time_ms = 0u64;
 
-    for chunk in routed_pages.chunks(OCR_PAGE_CHUNK_SIZE) {
+    for chunk in routed_pages.chunks(ocr_page_chunk_size(engine.preferred_page_concurrency())) {
         let native_chunk = chunk
             .iter()
             .map(|page_number| {
@@ -927,6 +938,19 @@ pub enum OcrPipelineError {
 mod tests {
     use super::*;
 
+    #[test]
+    fn chunk_size_scales_with_engine_concurrency() {
+        // Sequential engines keep the memory-bounding default.
+        assert_eq!(ocr_page_chunk_size(1), OCR_PAGE_CHUNK_SIZE);
+        // Parallel engines get three waves of work per batch.
+        assert_eq!(ocr_page_chunk_size(3), 9);
+        assert_eq!(ocr_page_chunk_size(2), 6);
+        // Engine-reported concurrency is untrusted: clamp before sizing so a
+        // misbehaving engine cannot inflate rendered-page memory or overflow.
+        assert_eq!(ocr_page_chunk_size(0), OCR_PAGE_CHUNK_SIZE);
+        assert_eq!(ocr_page_chunk_size(usize::MAX), 24);
+    }
+
     struct TrackingRenderer {
         batches: Mutex<Vec<Vec<u32>>>,
     }

tests/snapshots/real-estate-pricing.md

@@ -2,9 +2,19 @@
 
 # BePriced?
 
-*Commercial real estate pricing* **C O M M E R C I A L R E A L E S T A T E** pricingisliketheweather:everyonetalks *needs disciplined and systematic*about it, but few understand it. Most observers base “appropriate” real estate *analysis of the data.* pricing on historical norms. The cap rate—anindicatorofvaluerelativetosta- bilized net operating income (NOI) before capital expenditures, tenant improvement,andleasingcommissions— isthemostcommonlyusedmetricofreal estate pricing. But cap rates have been largelyunresponsivetoalternativeratesof return available to investors, with the **P E T E R L I N N E M A N** exception of BBB bonds, throughout
+*Commercial real estate pricing*
 
-8 4 Z E L L / L U R I E R E A L E S T A T E C E N T E R
+*needs disciplined and systematic*
+
+*analysis of the data.*
+
+**C O M M E R C I A L R E A L E S T A T E** pricingisliketheweather:everyonetalks about it, but few understand it. Most observers base “appropriate” real estate pricing on historical norms. The cap rate—anindicatorofvaluerelativetosta- bilized net operating income (NOI) before capital expenditures, tenant improvement,andleasingcommissions— isthemostcommonlyusedmetricofreal estate pricing. But cap rates have been largelyunresponsivetoalternativeratesof return available to investors, with the exception of BBB bonds, throughout
+
+C E N T E R
+
+**P E T E R L I N N E M A N**
+
+8 4 Z E L L / L U R I E R E A L E S T A T E
 
 **Table I:** Cap rate correlations **Cap Rate Correlation With:*** **BBB Corp** **10-Year Bond Yield S&P Dividend** **Treasury (10-15 yr) Yield** Multifamily 0.187 0.771 0.068 Industrial-0.221 0.748-0.307 CBD Office-0.449 0.694-0.458 Retail-0.181 0.649-02.58
 
@@ -13,9 +23,11 @@
  12 10 8 Percent 6 4 2 1982 1986 1990 1994 1998 2002 2006
 Apartment Retail ndustrial 10-yr reasury CBD Office
 
-most of the past twenty-five years (Table presented in Figure 2 with an eighteen-
+most of the past twenty-five years (Table
+
+I). Such a relationship defies investment theory,asrealestatepricingshouldchange as property risks and the returns of alter- nativeinvestmentschange. Figure1displaysNCREIFcapratesby property type compared to the ten-year Treasury yield. Because the National Council of Real Estate Investment Fiduciaries (NCREIF) cap rate data is seriouslyflawedduetoappraisallags,itis
+presented in Figure 2 with an eighteen- monthlag.Thisdataprovidesanoverview ofthepricingofinstitutionalqualityreal estate.Figure2reflectsthesecapratesnet of the ten-year Treasury yield. Since cap rate spreads are highly correlated across propertytypes(TableII),wecanspeakof “cap rates” without reference to property type with little loss of insight. Cap rate spreadswerenegativeintheearlytomid- 1980s, when purchasing real estate was
 
-I). Such a relationship defies investment monthlag.Thisdataprovidesanoverview theory,asrealestatepricingshouldchange ofthepricingofinstitutionalqualityreal as property risks and the returns of alter-estate.Figure2reflectsthesecapratesnet nativeinvestmentschange. of the ten-year Treasury yield. Since cap Figure1displaysNCREIFcapratesby rate spreads are highly correlated across property type compared to the ten-year propertytypes(TableII),wecanspeakof Treasury yield. Because the National “cap rates” without reference to property Council of Real Estate Investment type with little loss of insight. Cap rate Fiduciaries (NCREIF) cap rate data is spreadswerenegativeintheearlytomid- seriouslyflawedduetoappraisallags,itis 1980s, when purchasing real estate was
 R E V I E W 8 5
 
 **Figure 2:** Capratespreadsover10-yearTreasury