diff --git a/mineru/model/table/cls/mineru_table_ori_cls.py b/mineru/model/table/cls/mineru_table_ori_cls.py
index f1081197..250224b4 100644
--- a/mineru/model/table/cls/mineru_table_ori_cls.py
+++ b/mineru/model/table/cls/mineru_table_ori_cls.py
@@ -2,6 +2,7 @@
 
 from PIL import Image
 from collections import defaultdict
+import inspect
 from typing import List, Dict
 import cv2
 import numpy as np
@@ -129,11 +130,14 @@ class MineruTableOrientationClsModel:
             return None
         return img[ymin:ymax, xmin:xmax].copy()
 
-    def _build_orientation_score_task(self, label: str, img_bgr: np.ndarray) -> Dict:
-        """为单个角度构造评分任务，只做 det、抽样和切图，不执行 rec。"""
-        det_ocr_res = self.ocr_engine.ocr(img_bgr, rec=False)
-        det_res = det_ocr_res[0] if det_ocr_res else None
-        sampled_boxes = self._sample_det_boxes(det_res)
+    def _build_orientation_score_task_from_det_boxes(
+        self,
+        label: str,
+        img_bgr: np.ndarray,
+        det_boxes,
+    ) -> Dict:
+        """根据已有 OCR det 框构造评分任务，复用 0 度门控结果并统一裁图逻辑。"""
+        sampled_boxes = self._sample_det_boxes(det_boxes)
 
         img_crop_list = []
         for box in sampled_boxes:
@@ -149,6 +153,16 @@ class MineruTableOrientationClsModel:
             "crop_end": len(img_crop_list),
         }
 
+    def _build_orientation_score_task(self, label: str, img_bgr: np.ndarray) -> Dict:
+        """为单个角度构造评分任务，只做 det、抽样和切图，不执行 rec。"""
+        det_ocr_res = self.ocr_engine.ocr(img_bgr, rec=False)
+        det_res = det_ocr_res[0] if det_ocr_res else None
+        return self._build_orientation_score_task_from_det_boxes(
+            label,
+            img_bgr,
+            det_res,
+        )
+
     def _build_orientation_score_tasks(self, img_bgr: np.ndarray) -> List[Dict]:
         """为一张表构造 0/90/270 三个角度的评分任务。"""
         tasks = []
@@ -182,6 +196,9 @@ class MineruTableOrientationClsModel:
         score_by_label = {}
         rec_res = rec_res or []
         for task in tasks:
+            if "score" in task:
+                score_by_label[task["label"]] = task["score"]
+                continue
             crop_start = task.get("crop_start", 0)
             crop_end = task.get("crop_end", crop_start + task.get("crop_count", 0))
             task_rec_res = rec_res[crop_start:crop_end]
@@ -306,6 +323,24 @@ class MineruTableOrientationClsModel:
             )
         return resolution_groups
 
+    @classmethod
+    def _collect_orientation_images(cls, imgs: List[Dict]) -> list[Dict]:
+        """扁平收集有效表格图，首轮 det 的分桶和 batch 交给 OCR detector 内部处理。"""
+        orientation_imgs = []
+        for index, img in enumerate(imgs):
+            bgr_img = cls._to_bgr_table_image(img)
+            img_height, img_width = bgr_img.shape[:2]
+            if img_height <= 0 or img_width <= 0:
+                continue
+
+            orientation_imgs.append(
+                {
+                    "index": index,
+                    "table_img_bgr": bgr_img,
+                }
+            )
+        return orientation_imgs
+
     @classmethod
     def _pad_group_images(
         cls,
@@ -327,46 +362,136 @@ class MineruTableOrientationClsModel:
             batch_images.append(padded_img)
         return batch_images
 
+    def _batch_detect_text_boxes(
+        self,
+        img_list: list[np.ndarray],
+        det_batch_size: int,
+        tqdm_enable: bool = False,
+        tqdm_desc: str = "OCR-det Predict",
+        progress_bar=None,
+    ):
+        """统一调用 OCR detector batch_predict，并兼容不支持进度参数的测试替身。"""
+        if not img_list:
+            return []
+
+        max_batch_size = max(1, min(len(img_list), int(det_batch_size)))
+        batch_predict = self.ocr_engine.text_detector.batch_predict
+
+        progress_kwargs = {}
+        try:
+            signature = inspect.signature(batch_predict)
+            params = signature.parameters
+        except (TypeError, ValueError):
+            params = {}
+
+        if "tqdm_enable" in params:
+            progress_kwargs["tqdm_enable"] = tqdm_enable
+        if "tqdm_desc" in params:
+            progress_kwargs["tqdm_desc"] = tqdm_desc
+        if "tqdm_progress_bar" in params:
+            progress_kwargs["tqdm_progress_bar"] = progress_bar
+
+        batch_results = batch_predict(img_list, max_batch_size, **progress_kwargs)
+
+        if progress_bar is not None and "tqdm_progress_bar" not in progress_kwargs:
+            progress_bar.update(len(img_list))
+
+        return batch_results
+
     def _detect_rotation_candidates(
         self,
-        resolution_groups: Dict[tuple[int, int], list[Dict]],
+        orientation_imgs: list[Dict],
         det_batch_size: int,
-        resolution_group_stride: int,
+        tqdm_enable: bool = False,
+        tqdm_desc: str = "Table orientation",
         progress_bar=None,
     ) -> list[Dict]:
         """对表格批量做 OCR det，并筛选需要进入多角度评分的候选。"""
         rotated_imgs = []
-        for _group_key, group_imgs in resolution_groups.items():
-            batch_images = self._pad_group_images(group_imgs, resolution_group_stride)
-            batch_results = self.ocr_engine.text_detector.batch_predict(
-                batch_images,
-                max(1, min(len(batch_images), det_batch_size)),
-            )
+        batch_images = [img_info["table_img_bgr"] for img_info in orientation_imgs]
+        batch_results = self._batch_detect_text_boxes(
+            batch_images,
+            det_batch_size,
+            tqdm_enable=tqdm_enable and progress_bar is None,
+            tqdm_desc=f"{tqdm_desc} det",
+            progress_bar=progress_bar,
+        )
 
-            for img_info, (dt_boxes, _elapse) in zip(group_imgs, batch_results):
-                if self._is_rotation_candidate_by_det_boxes(dt_boxes):
-                    rotated_imgs.append(img_info)
-            if progress_bar is not None:
-                progress_bar.update(len(group_imgs))
+        for img_info, (dt_boxes, _elapse) in zip(orientation_imgs, batch_results):
+            if not self._is_rotation_candidate_by_det_boxes(dt_boxes):
+                continue
+            candidate_info = dict(img_info)
+            candidate_info["gate_det_boxes"] = dt_boxes
+            rotated_imgs.append(candidate_info)
         return rotated_imgs
 
+    @staticmethod
+    def _add_score_task_crops(task: Dict, all_crop_imgs: list[np.ndarray]) -> None:
+        """将有效评分任务加入 OCR-rec 输入，不足阈值的任务直接记为 0 分。"""
+        if task["crop_count"] < ORIENTATION_SCORE_MIN_VALID_RESULTS:
+            task["score"] = (0.0, 0, 0)
+            task["crop_start"] = len(all_crop_imgs)
+            task["crop_end"] = len(all_crop_imgs)
+            return
+
+        crop_start = len(all_crop_imgs)
+        all_crop_imgs.extend(task["crops"])
+        crop_end = len(all_crop_imgs)
+        task["crop_start"] = crop_start
+        task["crop_end"] = crop_end
+
     def _build_score_tasks_for_candidates(
         self,
         rotated_imgs: list[Dict],
+        det_batch_size: int,
         progress_bar=None,
     ) -> tuple[list[tuple[Dict, list[Dict]]], list[np.ndarray]]:
         """为所有旋转候选构造三角度评分任务，并汇总成一次 OCR rec 输入。"""
         img_score_tasks = []
         all_crop_imgs = []
+        score_det_images = []
+        score_det_tasks = []
+
         for img_info in rotated_imgs:
-            tasks = self._build_orientation_score_tasks(img_info["table_img_bgr"])
-            for task in tasks:
-                crop_start = len(all_crop_imgs)
-                all_crop_imgs.extend(task["crops"])
-                crop_end = len(all_crop_imgs)
-                task["crop_start"] = crop_start
-                task["crop_end"] = crop_end
+            table_img_bgr = img_info["table_img_bgr"]
+            tasks = [
+                self._build_orientation_score_task_from_det_boxes(
+                    "0",
+                    table_img_bgr,
+                    img_info.get("gate_det_boxes"),
+                )
+            ]
+            for label in ("90", "270"):
+                rotated_img = self._rotate_image_by_label(table_img_bgr, label)
+                task = {
+                    "label": label,
+                    "rotated_img_bgr": rotated_img,
+                    "crops": [],
+                    "crop_count": 0,
+                    "crop_start": 0,
+                    "crop_end": 0,
+                }
+                tasks.append(task)
+                score_det_images.append(rotated_img)
+                score_det_tasks.append(task)
             img_score_tasks.append((img_info, tasks))
+
+        score_det_results = self._batch_detect_text_boxes(
+            score_det_images,
+            det_batch_size,
+        )
+        for task, (dt_boxes, _elapse) in zip(score_det_tasks, score_det_results):
+            score_task = self._build_orientation_score_task_from_det_boxes(
+                task["label"],
+                task["rotated_img_bgr"],
+                dt_boxes,
+            )
+            task.update(score_task)
+            task.pop("rotated_img_bgr", None)
+
+        for _img_info, tasks in img_score_tasks:
+            for task in tasks:
+                self._add_score_task_crops(task, all_crop_imgs)
             if progress_bar is not None:
                 progress_bar.update(1)
         return img_score_tasks, all_crop_imgs
@@ -395,6 +520,7 @@ class MineruTableOrientationClsModel:
     def _score_rotation_candidates(
         self,
         rotated_imgs: list[Dict],
+        det_batch_size: int,
         tqdm_enable: bool = False,
         tqdm_desc: str = "Table orientation",
         progress_bar=None,
@@ -406,6 +532,7 @@ class MineruTableOrientationClsModel:
         label_by_index = {}
         img_score_tasks, all_crop_imgs = self._build_score_tasks_for_candidates(
             rotated_imgs,
+            det_batch_size,
             progress_bar=progress_bar,
         )
         self._extend_progress_total(progress_bar, len(all_crop_imgs))
@@ -437,13 +564,9 @@ class MineruTableOrientationClsModel:
         """
         批量预测传入表格图片的旋转角度，只返回角度，不修改输入图片。
         """
-        RESOLUTION_GROUP_STRIDE = 128
         rotate_labels = ["0"] * len(imgs)
-        resolution_groups = self._collect_orientation_image_groups(
-            imgs,
-            RESOLUTION_GROUP_STRIDE,
-        )
-        total_images = sum(len(group_imgs) for group_imgs in resolution_groups.values())
+        orientation_imgs = self._collect_orientation_images(imgs)
+        total_images = len(orientation_imgs)
         progress_bar = None
         if tqdm_enable:
             progress_bar = tqdm(
@@ -453,9 +576,10 @@ class MineruTableOrientationClsModel:
             )
         try:
             rotated_imgs = self._detect_rotation_candidates(
-                resolution_groups,
+                orientation_imgs,
                 det_batch_size,
-                RESOLUTION_GROUP_STRIDE,
+                tqdm_enable=tqdm_enable,
+                tqdm_desc=tqdm_desc,
                 progress_bar=progress_bar,
             )
             self._extend_progress_total(progress_bar, len(rotated_imgs))
@@ -464,6 +588,7 @@ class MineruTableOrientationClsModel:
                 progress_bar.refresh()
             label_by_index = self._score_rotation_candidates(
                 rotated_imgs,
+                det_batch_size,
                 tqdm_enable=tqdm_enable,
                 tqdm_desc=tqdm_desc,
                 progress_bar=progress_bar,