diff --git a/core/llm/__init__.py b/core/llm/__init__.py
index 50242000b..15773c121 100644
--- a/core/llm/__init__.py
+++ b/core/llm/__init__.py
@@ -8,6 +8,7 @@ from .t2a_decision import (
 )
 from .ocr_extractor import (
     extract_digits_tesseract_from_image,
+    extract_grid_from_image,
     extract_table_from_image,
     extract_text_from_image,
 )
@@ -19,5 +20,6 @@ __all__ = [
     "build_dpi_enriched",
     "extract_text_from_image",
     "extract_table_from_image",
+    "extract_grid_from_image",
     "extract_digits_tesseract_from_image",
 ]
diff --git a/core/llm/ocr_extractor.py b/core/llm/ocr_extractor.py
index e8fbd0f08..39b08fc1d 100644
--- a/core/llm/ocr_extractor.py
+++ b/core/llm/ocr_extractor.py
@@ -243,3 +243,107 @@ def extract_table_from_image(
     except Exception as e:
         logger.warning("extract_table échoué sur %s : %s", image_path, e)
         return []
+
+
+def _cluster_1d(centers: List[float], tol: float) -> List[Tuple[float, int]]:
+    """Regroupe des positions 1D par proximité (centres triés, gap > tol = nouveau cluster).
+
+    Retourne, pour chaque centre d'entrée (ordre d'origine), un couple
+    (centre_du_cluster, index_du_cluster), les clusters étant indexés dans
+    l'ordre croissant. Permet de mapper lignes (y) et colonnes (x).
+    """
+    order = sorted(range(len(centers)), key=lambda i: centers[i])
+    cluster_of = [0] * len(centers)
+    cluster_centers: List[List[float]] = []
+    prev = None
+    idx = -1
+    for i in order:
+        c = centers[i]
+        if prev is None or (c - prev) > tol:
+            idx += 1
+            cluster_centers.append([])
+        cluster_centers[idx].append(c)
+        cluster_of[i] = idx
+        prev = c
+    means = [sum(g) / len(g) for g in cluster_centers]
+    return [(means[cluster_of[i]], cluster_of[i]) for i in range(len(centers))]
+
+
+def extract_grid_from_image(
+    image_path: str,
+    region: Optional[Tuple[int, int, int, int]] = None,
+    row_tol: float = 12.0,
+    col_tol: float = 25.0,
+) -> List[List[dict]]:
+    """Extrait un tableau STRUCTURÉ (lignes ET colonnes) via OCR EasyOCR.
+
+    Contrairement à `extract_table_from_image` (liste plate triée par y, x jeté),
+    on conserve la coordonnée x pour reconstruire une grille. Clustering :
+    lignes par proximité du centre y, colonnes par proximité du centre x.
+
+    Args:
+        image_path: chemin du PNG sur disque.
+        region: (x, y, w, h) pour cropper avant OCR. None = image entière.
+        row_tol: écart vertical max (px) entre 2 tokens d'une même ligne.
+        col_tol: écart horizontal max (px) entre 2 tokens d'une même colonne.
+
+    Returns:
+        Grille `List[List[cell]]`, lignes top→bottom, colonnes left→right.
+        `cell = {"text", "bbox", "confidence", "row", "col"}`.
+        En cas d'erreur ou d'absence de tokens, retourne [].
+    """
+    path = Path(image_path)
+    if not path.exists():
+        logger.warning("extract_grid: fichier introuvable %s", image_path)
+        return []
+
+    try:
+        from PIL import Image
+        import numpy as np
+
+        img = Image.open(path)
+        if region:
+            x, y, w, h = region
+            img = img.crop((x, y, x + w, y + h))
+
+        reader = _get_reader()
+        results = reader.readtext(np.array(img), detail=1, paragraph=False)
+
+        toks = []
+        for bbox, text, conf in results:
+            t = str(text).strip()
+            if not t:
+                continue
+            xs = [p[0] for p in bbox]
+            ys = [p[1] for p in bbox]
+            toks.append({
+                "text": t,
+                "bbox": bbox,
+                "confidence": conf,
+                "xc": sum(xs) / len(xs),
+                "yc": sum(ys) / len(ys),
+            })
+        if not toks:
+            return []
+
+        rows_cl = _cluster_1d([tk["yc"] for tk in toks], row_tol)
+        cols_cl = _cluster_1d([tk["xc"] for tk in toks], col_tol)
+        for tk, (_yc, r), (_xc, c) in zip(toks, rows_cl, cols_cl):
+            tk["row"], tk["col"] = r, c
+
+        n_rows = max(tk["row"] for tk in toks) + 1
+        grid: List[List[dict]] = [[] for _ in range(n_rows)]
+        for tk in toks:
+            grid[tk["row"]].append({
+                "text": tk["text"],
+                "bbox": tk["bbox"],
+                "confidence": tk["confidence"],
+                "row": tk["row"],
+                "col": tk["col"],
+            })
+        for row in grid:
+            row.sort(key=lambda cell: cell["col"])
+        return grid
+    except Exception as e:
+        logger.warning("extract_grid échoué sur %s : %s", image_path, e)
+        return []
diff --git a/tests/unit/test_extract_grid.py b/tests/unit/test_extract_grid.py
new file mode 100644
index 000000000..fa4220576
--- /dev/null
+++ b/tests/unit/test_extract_grid.py
@@ -0,0 +1,79 @@
+"""Tests pour extract_grid_from_image — lecture de tableau STRUCTURÉE.
+
+Contrairement à extract_table_from_image (qui jette x et retourne une liste
+plate triée par y), extract_grid_from_image reconstruit une vraie grille
+List[List[cell]] : clustering des lignes par proximité y, des colonnes par
+proximité x. bbox + confiance conservées par cellule.
+
+Les tokens OCR sont injectés (mock du reader EasyOCR) → pas de PNG réel,
+pas de GPU.
+"""
+from pathlib import Path
+from types import SimpleNamespace
+
+from PIL import Image
+
+import core.llm.ocr_extractor as ocr_extractor
+
+
+def _blank_png(path: Path) -> None:
+    Image.new("RGB", (300, 120), "white").save(path)
+
+
+def _bbox(x0: float, y0: float, x1: float, y1: float):
+    """bbox EasyOCR = 4 points [tl, tr, br, bl], chaque point (x, y)."""
+    return [[x0, y0], [x1, y0], [x1, y1], [x0, y1]]
+
+
+def _fake_reader(tokens):
+    """Reader factice : readtext() renvoie la liste (bbox, text, conf) fournie."""
+    return SimpleNamespace(readtext=lambda *a, **k: tokens)
+
+
+def test_extract_grid_2x3(tmp_path, monkeypatch):
+    image_path = tmp_path / "table.png"
+    _blank_png(image_path)
+
+    # 2 lignes (y≈10 et y≈60) × 3 colonnes (x≈10, x≈110, x≈210).
+    # Volontairement mélangées dans l'ordre OCR pour vérifier le tri.
+    tokens = [
+        (_bbox(110, 58, 160, 78), "B2", 0.97),
+        (_bbox(10, 10, 60, 30), "A1", 0.91),
+        (_bbox(210, 12, 260, 32), "C1", 0.88),
+        (_bbox(210, 60, 260, 80), "C2", 0.95),
+        (_bbox(10, 60, 60, 80), "A2", 0.90),
+        (_bbox(110, 8, 160, 28), "B1", 0.93),
+    ]
+    monkeypatch.setattr(ocr_extractor, "_get_reader", lambda: _fake_reader(tokens))
+
+    grid = ocr_extractor.extract_grid_from_image(str(image_path))
+
+    # Grille 2×3 ordonnée
+    assert len(grid) == 2, "doit détecter 2 lignes"
+    assert all(len(row) == 3 for row in grid), "chaque ligne doit avoir 3 colonnes"
+
+    texts = [[cell["text"] for cell in row] for row in grid]
+    assert texts == [["A1", "B1", "C1"], ["A2", "B2", "C2"]]
+
+    # Métadonnées conservées + indices row/col cohérents
+    cell = grid[0][2]
+    assert cell["text"] == "C1"
+    assert cell["confidence"] == 0.88
+    assert cell["bbox"] == _bbox(210, 12, 260, 32)
+    assert cell["row"] == 0
+    assert cell["col"] == 2
+    assert grid[1][0]["row"] == 1 and grid[1][0]["col"] == 0
+
+
+def test_extract_grid_empty_when_no_tokens(tmp_path, monkeypatch):
+    image_path = tmp_path / "blank.png"
+    _blank_png(image_path)
+    monkeypatch.setattr(ocr_extractor, "_get_reader", lambda: _fake_reader([]))
+
+    grid = ocr_extractor.extract_grid_from_image(str(image_path))
+    assert grid == []
+
+
+def test_extract_grid_missing_file_returns_empty():
+    grid = ocr_extractor.extract_grid_from_image("/no/such/file.png")
+    assert grid == []