Fix:improve multi-column document detection (#11415)

### What problem does this PR solve?

change:
improve multi-column document detection

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

deepdoc/parser/pdf_parser.py

@@ -33,6 +33,8 @@ import xgboost as xgb
 from huggingface_hub import snapshot_download
 from PIL import Image
 from pypdf import PdfReader as pdf2_read
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
 
 from common.file_utils import get_project_base_directory
 from common.misc_utils import pip_install_torch
@@ -353,7 +355,6 @@ class RAGFlowPdfParser:
     def _assign_column(self, boxes, zoomin=3):
         if not boxes:
             return boxes
-
         if all("col_id" in b for b in boxes):
             return boxes
 
@@ -361,61 +362,80 @@ class RAGFlowPdfParser:
         for b in boxes:
             by_page[b["page_number"]].append(b)
 
-        page_info = {}  # pg -> dict(page_w, left_edge, cand_cols)
-        counter = Counter()
+        page_cols = {}
 
         for pg, bxs in by_page.items():
             if not bxs:
-                page_info[pg] = {"page_w": 1.0, "left_edge": 0.0, "cand": 1}
-                counter[1] += 1
+                page_cols[pg] = 1
                 continue
 
-            if hasattr(self, "page_images") and self.page_images and len(self.page_images) >= pg:
-                page_w = self.page_images[pg - 1].size[0] / max(1, zoomin)
-                left_edge = 0.0
-            else:
-                xs0 = [box["x0"] for box in bxs]
-                xs1 = [box["x1"] for box in bxs]
-                left_edge = float(min(xs0))
-                page_w = max(1.0, float(max(xs1) - left_edge))
+            x0s_raw = np.array([b["x0"] for b in bxs], dtype=float)
 
-            widths = [max(1.0, (box["x1"] - box["x0"])) for box in bxs]
-            median_w = float(np.median(widths)) if widths else 1.0
+            min_x0 = np.min(x0s_raw)
+            max_x1 = np.max([b["x1"] for b in bxs])
+            width = max_x1 - min_x0
 
-            raw_cols = int(page_w / max(1.0, median_w))
+            INDENT_TOL = width * 0.12
+            x0s = []
+            for x in x0s_raw:
+                if abs(x - min_x0) < INDENT_TOL:
+                    x0s.append([min_x0])
+                else:
+                    x0s.append([x])
+            x0s = np.array(x0s, dtype=float)
+            
+            max_try = min(4, len(bxs))
+            if max_try < 2:
+                max_try = 1
+            best_k = 1
+            best_score = -1
 
-            # cand = raw_cols if (raw_cols >= 2 and median_w < page_w / raw_cols * 0.8) else 1
-            cand = raw_cols
+            for k in range(1, max_try + 1):
+                km = KMeans(n_clusters=k, n_init="auto")
+                labels = km.fit_predict(x0s)
 
-            page_info[pg] = {"page_w": page_w, "left_edge": left_edge, "cand": cand}
-            counter[cand] += 1
+                centers = np.sort(km.cluster_centers_.flatten())
+                if len(centers) > 1:
+                    try:
+                        score = silhouette_score(x0s, labels)
+                    except ValueError:
+                        continue
+                else:
+                    score = 0
+                print(f"{k=},{score=}",flush=True)
+                if score > best_score:
+                    best_score = score
+                    best_k = k
 
-            logging.info(f"[Page {pg}] median_w={median_w:.2f}, page_w={page_w:.2f}, raw_cols={raw_cols}, cand={cand}")
+            page_cols[pg] = best_k
+            logging.info(f"[Page {pg}] best_score={best_score:.2f}, best_k={best_k}")
 
-        global_cols = counter.most_common(1)[0][0]
+
+        global_cols = Counter(page_cols.values()).most_common(1)[0][0]
         logging.info(f"Global column_num decided by majority: {global_cols}")
 
+
         for pg, bxs in by_page.items():
             if not bxs:
                 continue
+            k = page_cols[pg] 
+            if len(bxs) < k:
+                k = 1
+            x0s = np.array([[b["x0"]] for b in bxs], dtype=float)
+            km = KMeans(n_clusters=k, n_init="auto")
+            labels = km.fit_predict(x0s)
 
-            page_w = page_info[pg]["page_w"]
-            left_edge = page_info[pg]["left_edge"]
+            centers = km.cluster_centers_.flatten()
+            order = np.argsort(centers)
 
-            if global_cols == 1:
-                for box in bxs:
-                    box["col_id"] = 0
-                continue
+            remap = {orig: new for new, orig in enumerate(order)}
 
-            for box in bxs:
-                w = box["x1"] - box["x0"]
-                if w >= 0.8 * page_w:
-                    box["col_id"] = 0
-                    continue
-                cx = 0.5 * (box["x0"] + box["x1"])
-                norm_cx = (cx - left_edge) / page_w
-                norm_cx = max(0.0, min(norm_cx, 0.999999))
-                box["col_id"] = int(min(global_cols - 1, norm_cx * global_cols))
+            for b, lb in zip(bxs, labels):
+                b["col_id"] = remap[lb]
+            
+            grouped = defaultdict(list)
+            for b in bxs:
+                grouped[b["col_id"]].append(b)
 
         return boxes
 
@@ -1303,7 +1323,10 @@ class RAGFlowPdfParser:
 
         positions = []
         for ii, (pns, left, right, top, bottom) in enumerate(poss):
-            right = left + max_width
+            if 0 < ii < len(poss) - 1:
+                right = max(left + 10, right)
+            else:
+                right = left + max_width
             bottom *= ZM
             for pn in pns[1:]:
                 if 0 <= pn - 1 < page_count: