feat: enhance table OCR detection with batch processing and new input handling

mineru/backend/pipeline/batch_analyze.py

@@ -59,6 +59,7 @@ class BatchAnalyze:
         enable_ocr_det_batch: bool = True,
         table_ori_cls_batch_enabled: bool | None = None,
         text_ocr_det_batch_enabled: bool | None = None,
+        table_ocr_det_batch_enabled: bool | None = None,
         mask_inline_formula_for_ocr_det: bool = True,
     ):
         self.batch_ratio = batch_ratio
@@ -72,6 +73,9 @@ class BatchAnalyze:
         self.text_ocr_det_batch_enabled = (
             enable_ocr_det_batch if text_ocr_det_batch_enabled is None else text_ocr_det_batch_enabled
         )
+        self.table_ocr_det_batch_enabled = (
+            enable_ocr_det_batch if table_ocr_det_batch_enabled is None else table_ocr_det_batch_enabled
+        )
         self.mask_inline_formula_for_ocr_det = (
             get_ocr_det_mask_inline_formula_enable(mask_inline_formula_for_ocr_det)
         )
@@ -92,6 +96,72 @@ class BatchAnalyze:
             return bgr_image
         return self._apply_mask_boxes_to_image(bgr_image, mask_boxes)
 
+    def _build_table_ocr_det_items(self, table_res_list_all_page: list[dict]) -> list[dict]:
+        """构造表格 OCR-det 输入项，保留原图、遮罩图和后续回填所需信息。"""
+        table_det_items = []
+        for index, table_res_dict in enumerate(table_res_list_all_page):
+            bgr_image = cv2.cvtColor(table_res_dict["table_img"], cv2.COLOR_RGB2BGR)
+            table_inline_objects = (
+                table_res_dict.get("table_inline_objects", [])
+                if self._table_supports_inline_objects(table_res_dict)
+                else []
+            )
+            inline_mask_boxes = [
+                {"bbox": inline_object["table_rel_mask_bbox"]}
+                for inline_object in table_inline_objects
+            ]
+            formula_mask_boxes = [
+                {"bbox": inline_object["table_rel_mask_bbox"]}
+                for inline_object in table_inline_objects
+                if inline_object["kind"] == "formula"
+            ]
+            det_image = (
+                self._apply_mask_boxes_to_image(bgr_image, inline_mask_boxes)
+                if inline_mask_boxes
+                else bgr_image
+            )
+            table_det_items.append(
+                {
+                    "bgr_image": bgr_image,
+                    "det_image": det_image,
+                    "formula_mask_boxes": formula_mask_boxes,
+                    "lang": table_res_dict["lang"],
+                    "table_id": index,
+                }
+            )
+        return table_det_items
+
+    def _append_table_ocr_det_result(
+        self,
+        table_det_item: dict,
+        dt_boxes,
+        rec_img_lang_group: dict,
+    ) -> None:
+        """将单表 OCR-det 结果整理成 OCR-rec 输入，并保持表格回填顺序。"""
+        if dt_boxes is None or len(dt_boxes) == 0:
+            return
+
+        ocr_result = dt_boxes
+        formula_mask_boxes = table_det_item["formula_mask_boxes"]
+        if formula_mask_boxes:
+            ocr_result = update_det_boxes(ocr_result, formula_mask_boxes)
+            if not ocr_result:
+                return
+
+        ocr_result = sorted_boxes(ocr_result)
+        for dt_box in ocr_result:
+            dt_box_array = np.asarray(dt_box, dtype=np.float32)
+            rec_img_lang_group.setdefault(table_det_item["lang"], []).append(
+                {
+                    "cropped_img": get_rotate_crop_image_for_text_rec(
+                        table_det_item["bgr_image"],
+                        dt_box_array.copy(),
+                    ),
+                    "dt_box": dt_box_array.copy(),
+                    "table_id": table_det_item["table_id"],
+                }
+            )
+
     @staticmethod
     def _prune_empty_ocr_text_blocks(layout_res: list[dict], ocr_enable: bool) -> None:
         if not ocr_enable or not layout_res:
@@ -487,7 +557,7 @@ class BatchAnalyze:
                     f"Table classification failed: {e}, using default model"
                 )
 
-            # OCR det 过程，顺序执行
+            # OCR det 过程，默认使用 detector 内部分桶 batch，关闭开关时回退逐表单张路径。
             rec_img_lang_group = defaultdict(list)
             det_ocr_engine = atom_model_manager.get_atom_model(
                 atom_model_name=AtomicModel.OCR,
@@ -495,46 +565,40 @@ class BatchAnalyze:
                 det_db_unclip_ratio=1.6,
                 enable_merge_det_boxes=False,
             )
-            for index, table_res_dict in enumerate(
-                    tqdm(table_res_list_all_page, desc="Table-ocr det")
-            ):
-                bgr_image = cv2.cvtColor(table_res_dict["table_img"], cv2.COLOR_RGB2BGR)
-                table_inline_objects = (
-                    table_res_dict.get("table_inline_objects", [])
-                    if self._table_supports_inline_objects(table_res_dict)
-                    else []
-                )
-                inline_mask_boxes = [
-                    {"bbox": inline_object["table_rel_mask_bbox"]}
-                    for inline_object in table_inline_objects
-                ]
-                formula_mask_boxes = [
-                    {"bbox": inline_object["table_rel_mask_bbox"]}
-                    for inline_object in table_inline_objects
-                    if inline_object["kind"] == "formula"
-                ]
-                det_image = (
-                    self._apply_mask_boxes_to_image(bgr_image, inline_mask_boxes)
-                    if inline_mask_boxes
-                    else bgr_image
-                )
-                ocr_result = run_ocr_inference(
-                    det_ocr_engine.ocr, det_image, rec=False
-                )[0]
-                if ocr_result and formula_mask_boxes:
-                    ocr_result = update_det_boxes(ocr_result, formula_mask_boxes)
-                if ocr_result:
-                    ocr_result = sorted_boxes(ocr_result)
-                # 构造需要 OCR 识别的图片字典，包括cropped_img, dt_box, table_id，并按照语言进行分组
-                for dt_box in ocr_result:
-                    rec_img_lang_group[table_res_dict["lang"]].append(
-                        {
-                            "cropped_img": get_rotate_crop_image_for_text_rec(
-                                bgr_image, np.asarray(dt_box, dtype=np.float32)
-                            ),
-                            "dt_box": np.asarray(dt_box, dtype=np.float32),
-                            "table_id": index,
-                        }
+            table_det_items = self._build_table_ocr_det_items(table_res_list_all_page)
+            if self.table_ocr_det_batch_enabled:
+                det_images = [table_det_item["det_image"] for table_det_item in table_det_items]
+                if det_images:
+                    det_batch_size = max(
+                        1,
+                        min(len(det_images), self.batch_ratio * OCR_DET_BASE_BATCH_SIZE),
+                    )
+                    batch_results = run_ocr_inference(
+                        det_ocr_engine.text_detector.batch_predict,
+                        det_images,
+                        det_batch_size,
+                        tqdm_enable=True,
+                        tqdm_desc="Table-ocr det",
+                    )
+                    if len(batch_results) != len(table_det_items):
+                        raise ValueError("Table OCR det batch result count mismatch")
+                    for table_det_item, (dt_boxes, _) in zip(table_det_items, batch_results):
+                        self._append_table_ocr_det_result(
+                            table_det_item,
+                            dt_boxes,
+                            rec_img_lang_group,
+                        )
+            else:
+                for table_det_item in tqdm(table_det_items, desc="Table-ocr det"):
+                    ocr_result = run_ocr_inference(
+                        det_ocr_engine.ocr,
+                        table_det_item["det_image"],
+                        rec=False,
+                    )[0]
+                    self._append_table_ocr_det_result(
+                        table_det_item,
+                        ocr_result,
+                        rec_img_lang_group,
                     )
 
             # OCR rec，按照语言分批处理

mineru/utils/enum_class.py

@@ -104,7 +104,6 @@ class ModelPath:
     pytorch_paddle = "models/OCR/paddleocr_torch"
     slanet_plus = "models/TabRec/SlanetPlus/slanet-plus.onnx"
     unet_structure = "models/TabRec/UnetStructure/unet.onnx"
-    paddle_table_cls = "models/TabCls/paddle_table_cls/PP-LCNet_x1_0_table_cls.onnx"
 
 
 class SplitFlag: