feat(ocr): improve text detection and OCR accuracy

- Update OCR utils to handle different box formats and improve angle calculation
- Modify PDF extraction kit to support OCR option and optimize processing flow
- Enhance PPOCR model to sort and filter detection boxes, improving text splitting accuracy

magic_pdf/model/pdf_extract_kit.py

@@ -116,7 +116,6 @@ class CustomPEKModel:
                 device=self.device
             )
         # 初始化ocr
-        if self.apply_ocr:
         self.ocr_model = atom_model_manager.get_atom_model(
             atom_model_name=AtomicModel.OCR,
             ocr_show_log=show_log,
@@ -174,7 +173,6 @@ class CustomPEKModel:
         ocr_res_list, table_res_list, single_page_mfdetrec_res = get_res_list_from_layout_res(layout_res)
 
         # ocr识别
-        if self.apply_ocr:
         ocr_start = time.time()
         # Process each area that requires OCR processing
         for res in ocr_res_list:
@@ -183,7 +181,10 @@ class CustomPEKModel:
 
             # OCR recognition
             new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
+            if self.apply_ocr:
                 ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res)[0]
+            else:
+                ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res, rec=False)[0]
 
             # Integration results
             if ocr_res:
@@ -191,7 +192,10 @@ class CustomPEKModel:
                 layout_res.extend(ocr_result_list)
 
         ocr_cost = round(time.time() - ocr_start, 2)
+        if self.apply_ocr:
             logger.info(f"ocr time: {ocr_cost}")
+        else:
+            logger.info(f"det time: {ocr_cost}")
 
         # 表格识别 table recognition
         if self.apply_table:

magic_pdf/model/sub_modules/ocr/paddleocr/ocr_utils.py

@@ -211,16 +211,21 @@ def get_ocr_result_list(ocr_res, useful_list):
     ocr_result_list = []
     for box_ocr_res in ocr_res:
 
+        if len(box_ocr_res) == 2:
             p1, p2, p3, p4 = box_ocr_res[0]
             text, score = box_ocr_res[1]
+        else:
+            p1, p2, p3, p4 = box_ocr_res
+            text, score = "", 1
         # average_angle_degrees = calculate_angle_degrees(box_ocr_res[0])
         # if average_angle_degrees > 0.5:
-        if calculate_is_angle(box_ocr_res[0]):
+        poly = [p1, p2, p3, p4]
+        if calculate_is_angle(poly):
             # logger.info(f"average_angle_degrees: {average_angle_degrees}, text: {text}")
             # 与x轴的夹角超过0.5度，对边界做一下矫正
             # 计算几何中心
-            x_center = sum(point[0] for point in box_ocr_res[0]) / 4
-            y_center = sum(point[1] for point in box_ocr_res[0]) / 4
+            x_center = sum(point[0] for point in poly) / 4
+            y_center = sum(point[1] for point in poly) / 4
             new_height = ((p4[1] - p1[1]) + (p3[1] - p2[1])) / 2
             new_width = p3[0] - p1[0]
             p1 = [x_center - new_width / 2, y_center - new_height / 2]

magic_pdf/model/sub_modules/ocr/paddleocr/ppocr_273_mod.py

@@ -78,9 +78,18 @@ class ModifiedPaddleOCR(PaddleOCR):
             for idx, img in enumerate(imgs):
                 img = preprocess_image(img)
                 dt_boxes, elapse = self.text_detector(img)
-                if not dt_boxes:
+                if dt_boxes is None:
                     ocr_res.append(None)
                     continue
+                dt_boxes = sorted_boxes(dt_boxes)
+                # merge_det_boxes 和 update_det_boxes 都会把poly转成bbox再转回poly，因此需要过滤所有倾斜程度较大的文本框
+                dt_boxes = merge_det_boxes(dt_boxes)
+                if mfd_res:
+                    bef = time.time()
+                    dt_boxes = update_det_boxes(dt_boxes, mfd_res)
+                    aft = time.time()
+                    logger.debug("split text box by formula, new dt_boxes num : {}, elapsed : {}".format(
+                        len(dt_boxes), aft - bef))
                 tmp_res = [box.tolist() for box in dt_boxes]
                 ocr_res.append(tmp_res)
             return ocr_res