fix: generic fuzzy text matching for overlay word-box positioning

Replace sequential 1:1 token-to-box mapping with fuzzy text matching.
Each token from cell.text finds its best matching word_box by text
similarity (normalized prefix match + substring bonus). Handles:
- Reordered boxes (different sort between text and boxes)
- IPA corrections changing token boundaries
- Token/box count mismatches
Unmatched tokens get interpolated positions from matched neighbors.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

admin-lehrer/components/ocr-overlay/useSlideWordPositions.ts

@@ -47,22 +47,20 @@ export function useSlideWordPositions(
 
       if (hasWordBoxes) {
         // --- WORD-BOX PATH: use OCR positions with cell.text tokens ---
+        // Uses fuzzy text matching to pair each token with its best box,
+        // handling reordering, IPA corrections, and token count mismatches.
         const positions = new Map<string, WordPosition[]>()
 
         for (const cell of cells) {
           if (!cell.bbox_pct || !cell.text) continue
 
-          // Tokens from the CLEANED cell text (reading order)
           const tokens = cell.text.split(/\s+/).filter(Boolean)
           if (tokens.length === 0) continue
 
-          // Word boxes sorted left-to-right
           const boxes = (cell.word_boxes || [])
             .filter(wb => wb.text.trim())
-            .sort((a, b) => a.left - b.left)
 
           if (boxes.length === 0) {
-            // No boxes — place all tokens at cell start as fallback
             const fallbackW = cell.bbox_pct.w / tokens.length
             const wordPos = tokens.map((t, i) => ({
               xPct: cell.bbox_pct.x + i * fallbackW,
@@ -74,47 +72,79 @@ export function useSlideWordPositions(
             continue
           }
 
+          // Match each token to its best box by text similarity.
+          // Normalize: lowercase, strip brackets/punctuation for comparison.
+          const norm = (s: string) => s.toLowerCase().replace(/[^a-z0-9äöüß]/g, '')
+
+          const used = new Set<number>()
+          const tokenBoxIdx: (number | null)[] = []
+
+          for (const token of tokens) {
+            const tn = norm(token)
+            let bestIdx = -1
+            let bestScore = 0
+
+            for (let bi = 0; bi < boxes.length; bi++) {
+              if (used.has(bi)) continue
+              const bn = norm(boxes[bi].text)
+              // Score: length of common prefix / max length
+              let common = 0
+              const minLen = Math.min(tn.length, bn.length)
+              for (let k = 0; k < minLen; k++) {
+                if (tn[k] === bn[k]) common++
+                else break
+              }
+              // Also check if token is a substring of box text or vice versa
+              const containsBonus = (bn.includes(tn) || tn.includes(bn)) ? 0.5 : 0
+              const score = (minLen > 0 ? common / Math.max(tn.length, bn.length) : 0) + containsBonus
+              if (score > bestScore) {
+                bestScore = score
+                bestIdx = bi
+              }
+            }
+
+            if (bestIdx >= 0 && bestScore > 0.2) {
+              used.add(bestIdx)
+              tokenBoxIdx.push(bestIdx)
+            } else {
+              tokenBoxIdx.push(null) // no match
+            }
+          }
+
+          // Build positions: matched tokens get box positions,
+          // unmatched tokens get interpolated between neighbors.
           const wordPos: WordPosition[] = []
 
-          if (tokens.length <= boxes.length) {
+          for (let ti = 0; ti < tokens.length; ti++) {
-            // More boxes than tokens: assign each token to a box in order.
+            const bi = tokenBoxIdx[ti]
-            // This handles the common case where box count matches or
+            if (bi !== null) {
-            // exceeds token count (e.g. OCR found extra fragments).
+              const box = boxes[bi]
-            for (let ti = 0; ti < tokens.length; ti++) {
-              const box = boxes[ti]
               wordPos.push({
                 xPct: (box.left / imgW) * 100,
                 wPct: (box.width / imgW) * 100,
                 text: tokens[ti],
                 fontRatio: 1.0,
               })
-            }
+            } else {
-          } else {
+              // Interpolate: find nearest matched neighbor before/after
-            // More tokens than boxes: assign boxes to first N tokens,
+              let prevPx = cell.bbox_pct.x / 100 * imgW
-            // then spread remaining tokens after the last box.
+              let prevW = 0
-            for (let ti = 0; ti < boxes.length; ti++) {
+              for (let p = ti - 1; p >= 0; p--) {
-              const box = boxes[ti]
+                if (tokenBoxIdx[p] !== null) {
+                  const pb = boxes[tokenBoxIdx[p]!]
+                  prevPx = pb.left + pb.width + 5
+                  prevW = pb.width
+                  break
+                }
+              }
+              const estW = prevW > 0 ? prevW : (cell.bbox_pct.w / 100 * imgW / tokens.length)
               wordPos.push({
-                xPct: (box.left / imgW) * 100,
+                xPct: (prevPx / imgW) * 100,
-                wPct: (box.width / imgW) * 100,
+                wPct: (estW / imgW) * 100,
                 text: tokens[ti],
                 fontRatio: 1.0,
               })
             }
-            // Remaining tokens: estimate position after last box
-            const lastBox = boxes[boxes.length - 1]
-            let cursorPx = lastBox.left + lastBox.width + 5
-            for (let ti = boxes.length; ti < tokens.length; ti++) {
-              // Estimate width from average box width
-              const avgW = boxes.reduce((s, b) => s + b.width, 0) / boxes.length
-              wordPos.push({
-                xPct: (cursorPx / imgW) * 100,
-                wPct: (avgW / imgW) * 100,
-                text: tokens[ti],
-                fontRatio: 1.0,
-              })
-              cursorPx += avgW + 5
-            }
           }
 
           if (wordPos.length > 0) {