bump version to 0.7.1

Two fixes for tables that have clean extractable text but fail heuristic
structure detection:

1. Numeric column merge pass (grid.rs): After initial X-position
   clustering, adjacent clusters are merged when one is sparse (header
   text) and the other is dense with >50% numeric items (data column).
   Multi-line wrapped headers often land slightly offset from their
   data column — the merge closes gaps within 1.5× the clustering
   threshold. New is_numeric_text() helper matches decimals, percentages,
   negative numbers, and comma-separated thousands.

2. Duplicate-header skip (detect_heuristic.rs): Spanning super-headers
   like "First Degree | First Degree | Higher Degree" contain duplicate
   cells that trigger looks_like_partial_table_ex rejection. Now skips
   rows with duplicate cells when a better header candidate exists
   within the next 3 rows (higher fill ratio or numeric cells).

Tested on BITS Pilani university report (430 pages, 314 table pages).
Page 4 (multi-line header + numeric data) previously returned
needs_ocr=true; now correctly detects the table structure.

Eval: 197 PDFs, zero regressions, all 104+ tests pass, zero clippy.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

napi/package.json

@@ -1,6 +1,6 @@
 {
   "name": "firecrawl-pdf-inspector",
-  "version": "0.7.0",
+  "version": "0.7.1",
   "description": "Fast PDF classification and text extraction. Detect text-based vs scanned PDFs, extract text by region with quality checks. Native Rust performance via napi-rs.",
   "main": "index.js",
   "types": "index.d.ts",

src/tables/detect_heuristic.rs

@@ -1144,6 +1144,33 @@ pub(crate) fn find_first_table_row(
             continue;
         }
 
+        // Skip rows that have duplicate non-empty cells. These are spanning
+        // super-headers (e.g., "First Degree | First Degree | Higher Degree")
+        // that sit above the real column header row. Using them as the markdown
+        // header produces duplicate column names that downstream validation
+        // rejects. Only skip if a subsequent row looks like a better header
+        // (denser fill or has data).
+        if filled_count >= 2 && !has_data {
+            let mut text_counts: std::collections::HashMap<&str, usize> =
+                std::collections::HashMap::new();
+            for cell in &filled_cells {
+                *text_counts.entry(cell.trim()).or_insert(0) += 1;
+            }
+            let has_duplicates = text_counts.values().any(|&count| count >= 2);
+            if has_duplicates {
+                // Check if a later row is a better header candidate
+                let has_better_below = cells.iter().skip(row_idx + 1).take(3).any(|r| {
+                    let next_filled = r.iter().filter(|c| !c.trim().is_empty()).count();
+                    let next_fill = next_filled as f32 / total_cols as f32;
+                    let next_numeric = r.iter().filter(|c| looks_like_number(c.trim())).count();
+                    next_fill >= 0.4 || next_numeric >= 2
+                });
+                if has_better_below {
+                    continue;
+                }
+            }
+        }
+
         // Data rows are definitely table content
         if has_data {
             first_table_row = row_idx;

src/tables/grid.rs

@@ -82,33 +82,42 @@ pub(crate) fn find_column_boundaries(
         }
     }
 
-    let mut columns = Vec::new();
-    let mut cluster_items: Vec<f32> = vec![x_positions[0]];
+    // Track cluster membership: for each cluster, store the list of x positions
+    let mut cluster_xs: Vec<Vec<f32>> = vec![vec![x_positions[0]]];
 
     for &x in &x_positions[1..] {
+        let last_cluster = cluster_xs.last().unwrap();
         // For dense columns (gap-histogram triggered), use edge-based clustering:
         // compare with the last item to avoid center-drift that merges adjacent
         // narrow columns.  For normal tables, use center-based (original behavior).
         let reference = if use_edge_clustering {
-            *cluster_items.last().unwrap()
+            *last_cluster.last().unwrap()
         } else {
-            cluster_items.iter().sum::<f32>() / cluster_items.len() as f32
+            last_cluster.iter().sum::<f32>() / last_cluster.len() as f32
         };
 
         if x - reference > cluster_threshold {
-            let cluster_center = cluster_items.iter().sum::<f32>() / cluster_items.len() as f32;
-            columns.push(cluster_center);
-            cluster_items = vec![x];
+            cluster_xs.push(vec![x]);
         } else {
-            cluster_items.push(x);
+            cluster_xs.last_mut().unwrap().push(x);
         }
     }
 
-    // Don't forget last cluster
-    if !cluster_items.is_empty() {
-        columns.push(cluster_items.iter().sum::<f32>() / cluster_items.len() as f32);
+    // Numeric column merge pass: when a sparse cluster (few items, typically
+    // header text) is adjacent to a dense numeric cluster and within 1.5×
+    // threshold, merge them. This fixes tables where multi-line wrapped
+    // headers have slightly different X positions than the data columns,
+    // causing the header and data to split into separate clusters.
+    let columns_before_merge = cluster_xs.len();
+    if columns_before_merge >= 3 {
+        cluster_xs = merge_numeric_adjacent_clusters(cluster_xs, items, cluster_threshold);
     }
 
+    let columns: Vec<f32> = cluster_xs
+        .iter()
+        .map(|xs| xs.iter().sum::<f32>() / xs.len() as f32)
+        .collect();
+
     // Filter columns - each should have multiple items
     let min_items_per_col = (items.len() / columns.len().max(1) / 4).max(2);
     let columns: Vec<f32> = columns
@@ -123,8 +132,9 @@ pub(crate) fn find_column_boundaries(
         .collect();
 
     log::debug!(
-        "  find_column_boundaries: {} columns before filter, threshold={:.1}, {} items",
+        "  find_column_boundaries: {} columns (merged from {}), threshold={:.1}, {} items",
         columns.len(),
+        columns_before_merge,
         cluster_threshold,
         items.len()
     );
@@ -148,6 +158,116 @@ pub(crate) fn find_column_boundaries(
     columns
 }
 
+/// Check if a text string looks like a number (digits, decimals, sign, comma).
+fn is_numeric_text(s: &str) -> bool {
+    let s = s.trim();
+    if s.is_empty() {
+        return false;
+    }
+    // Match patterns like: 8.23, -1.05, 9.99, 7.12, 100, 3,456.78, +5%, ---
+    // But NOT: BIO, Department, Core Courses
+    s.chars()
+        .all(|c| c.is_ascii_digit() || c == '.' || c == ',' || c == '-' || c == '+' || c == '%')
+        && s.chars().any(|c| c.is_ascii_digit())
+}
+
+/// Merge adjacent X-position clusters when one is a sparse header cluster
+/// and the other is a dense numeric data cluster. This prevents multi-line
+/// wrapped headers from splitting a logical column into two clusters.
+fn merge_numeric_adjacent_clusters(
+    mut clusters: Vec<Vec<f32>>,
+    items: &[(usize, &TextItem)],
+    threshold: f32,
+) -> Vec<Vec<f32>> {
+    // For each cluster, compute: center, item count, numeric fraction
+    struct ClusterInfo {
+        center: f32,
+        count: usize,
+        numeric_frac: f32,
+    }
+
+    let compute_info = |xs: &[f32]| -> ClusterInfo {
+        let center = xs.iter().sum::<f32>() / xs.len() as f32;
+        // Count items and numeric fraction for items near this cluster center
+        let mut total = 0;
+        let mut numeric = 0;
+        for (_, item) in items {
+            if (item.x - center).abs() < threshold {
+                total += 1;
+                if is_numeric_text(&item.text) {
+                    numeric += 1;
+                }
+            }
+        }
+        ClusterInfo {
+            center,
+            count: total,
+            numeric_frac: if total > 0 {
+                numeric as f32 / total as f32
+            } else {
+                0.0
+            },
+        }
+    };
+
+    // Merge distance: allow merging clusters that are slightly beyond the
+    // original threshold. Use 1.5× threshold to catch header-vs-data splits.
+    let merge_dist = threshold * 1.5;
+
+    // Iterate and merge adjacent pairs. Use a simple left-to-right scan.
+    let mut merged = true;
+    while merged {
+        merged = false;
+        let mut i = 0;
+        while i + 1 < clusters.len() {
+            let info_a = compute_info(&clusters[i]);
+            let info_b = compute_info(&clusters[i + 1]);
+            let dist = (info_b.center - info_a.center).abs();
+
+            if dist > merge_dist {
+                i += 1;
+                continue;
+            }
+
+            // Determine if one cluster is sparse (header) and the other
+            // is dense and numeric (data). A cluster is "sparse" if it has
+            // significantly fewer items than the other.
+            let (sparse, dense) = if info_a.count < info_b.count {
+                (&info_a, &info_b)
+            } else {
+                (&info_b, &info_a)
+            };
+
+            // Merge if the dense cluster is predominantly numeric (>50%)
+            // and the sparse cluster has at most 1/3 the items of the dense one.
+            let should_merge =
+                dense.numeric_frac > 0.50 && sparse.count <= dense.count / 2 && sparse.count <= 5;
+
+            if should_merge {
+                log::debug!(
+                    "  merging column clusters: center {:.1} ({} items, {:.0}% numeric) + {:.1} ({} items, {:.0}% numeric), dist={:.1}",
+                    info_a.center,
+                    info_a.count,
+                    info_a.numeric_frac * 100.0,
+                    info_b.center,
+                    info_b.count,
+                    info_b.numeric_frac * 100.0,
+                    dist,
+                );
+                // Merge cluster i+1 into cluster i
+                let next = clusters.remove(i + 1);
+                clusters[i].extend(next);
+                merged = true;
+                // Don't increment i — check if the merged cluster can merge further
+            } else {
+                i += 1;
+            }
+        }
+    }
+
+    clusters
+}
+
 /// Find row boundaries by clustering Y positions
 pub(crate) fn find_row_boundaries(items: &[(usize, &TextItem)]) -> Vec<f32> {
     let mut y_positions: Vec<f32> = items.iter().map(|(_, i)| i.y).collect();

tests/integration_tests.rs

@@ -1438,6 +1438,42 @@ fn test_extract_tables_in_regions_nonexistent_page() {
     assert!(region.text.is_empty());
 }
 
+#[test]
+fn test_bits_pilani_page4_table_detection() {
+    // Page 4 (0-indexed 3) has a table with multi-line wrapped headers and
+    // numeric data columns. The heuristic detector previously failed because:
+    // 1. Header items at different X positions than data created extra column
+    //    clusters (6 cols instead of 4)
+    // 2. Spanning super-header row ("First Degree | First Degree") produced
+    //    duplicate header cells that looks_like_partial_table_ex rejected
+    let buf = std::fs::read("tests/fixtures/bits_pilani_feedback.pdf").unwrap();
+    let results =
+        extract_tables_in_regions_mem(&buf, &[(3, vec![[0.0, 0.0, 612.0, 792.0]])]).unwrap();
+    assert_eq!(results.len(), 1);
+    let region = &results[0].regions[0];
+    assert!(
+        !region.needs_ocr,
+        "Page 4 table should be detected, got needs_ocr=true"
+    );
+    assert!(
+        region.text.contains("BIO"),
+        "Should contain department name BIO"
+    );
+    assert!(region.text.contains("8.23"), "Should contain numeric data");
+}
+
+#[test]
+fn test_bits_pilani_page8_table_detection() {
+    // Page 8 (0-indexed 7) has a numbered-row table that already worked.
+    // Verify it still works after changes.
+    let buf = std::fs::read("tests/fixtures/bits_pilani_feedback.pdf").unwrap();
+    let results =
+        extract_tables_in_regions_mem(&buf, &[(7, vec![[0.0, 0.0, 612.0, 792.0]])]).unwrap();
+    assert_eq!(results.len(), 1);
+    let region = &results[0].regions[0];
+    assert!(!region.needs_ocr, "Page 8 table should still be detected");
+}
+
 // =========================================================================
 // extract_pages_markdown_mem tests
 // =========================================================================