Update table_rec_v2 interface (#3608)

* Update table_rec_v2 interface

* Update

Author: Bobholamovic

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition.en.md

@@ -870,7 +870,7 @@ In the above Python script, the following steps are executed:
 </tr>
 <td><code>use_table_cells_ocr_results</code></td>
 <td>Whether to enable Table-Cells-OCR mode, when not enabled, use global OCR result to fill to HTML table, when enabled, do OCR cell by cell and fill to HTML table (it will increase the time consuming). Both of them perform differently in different scenarios, please choose according to the actual situation.</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> or <code>False</code>
@@ -1249,6 +1249,12 @@ Below are the API references for basic serving deployment and multi-language ser
 <td>Please refer to the description of the <code>text_rec_score_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td>
 <td>No</td>
 </tr>
+<tr>
+<td><code>useTableCellsOcrResults</code></td>
+<td><code>boolean</code></td>
+<td>Please refer to the description of the <code>use_table_cells_ocr_results</code> parameter of the pipeline object's <code>predict</code> method.</td>
+<td>No</td>
+</tr>
 </tbody>
 </table>
 

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition.md

@@ -815,7 +815,7 @@ for res in output:
 </tr>
 <td><code>use_table_cells_ocr_results</code></td>
 <td>是否启用单元格OCR模式，不启用时采用全局OCR结果填充至HTML表格，启用时逐个单元格做OCR并填充至HTML表格（会增加耗时）。二者在不同场景下性能不同，请根据实际情况选择。</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> 或者 <code>False</code>
@@ -1194,6 +1194,12 @@ for res in output:
 <td>请参阅产线对象中 <code>predict</code> 方法的 <code>text_rec_score_thresh</code> 参数相关说明。</td>
 <td>否</td>
 </tr>
+<tr>
+<td><code>useTableCellsOcrResults</code></td>
+<td><code>boolean</code></td>
+<td>请参阅产线对象中 <code>predict</code> 方法的 <code>use_table_cells_ocr_results</code> 参数相关说明。</td>
+<td>否</td>
+</tr>
 </tbody>
 </table>
 <ul>

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.en.md

@@ -893,23 +893,23 @@ In the above Python script, the following steps are executed:
 </tr>
 <td><code>use_table_cells_ocr_results</code></td>
 <td>Whether to enable Table-Cells-OCR mode, when not enabled, use global OCR result to fill to HTML table, when enabled, do OCR cell by cell and fill to HTML table (it will increase the time consuming). Both of them perform differently in different scenarios, please choose according to the actual situation.</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> or <code>False</code>
 <td><code>False</code></td>
 </tr>
 <td><code>use_e2e_wired_table_rec_model</code></td>
 <td>Whether to enable the wired table end-to-end prediction mode, when not enabled, using the table cells detection model prediction results filled to the HTML table, when enabled, using the end-to-end table structure recognition model cell prediction results filled to the HTML table. Both of them have different performance in different scenarios, please choose according to the actual situation.</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> or <code>False</code>
 <td><code>False</code></td>
 </tr>
 <td><code>use_e2e_wireless_table_rec_model</code></td>
 <td>Whether to enable the wireless table end-to-end prediction mode, when not enabled, using the table cells detection model prediction results filled to the HTML table, when enabled, using the end-to-end table structure recognition model cell prediction results filled to the HTML table. Both of them have different performance in different scenarios, please choose according to the actual situation.</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> or <code>False</code>
@@ -1322,6 +1322,24 @@ Below are the API references for basic serving deployment and multi-language ser
 <td>Please refer to the description of the <code>text_rec_score_thresh</code> parameter of the pipeline object's <code>predict</code> method.</td>
 <td>No</td>
 </tr>
+<tr>
+<td><code>useTableCellsOcrResults</code></td>
+<td><code>boolean</code></td>
+<td>Please refer to the description of the <code>use_table_cells_ocr_results</code> parameter of the pipeline object's <code>predict</code> method.</td>
+<td>No</td>
+</tr>
+<tr>
+<td><code>useE2eWiredTableRecModel</code></td>
+<td><code>boolean</code></td>
+<td>Please refer to the description of the <code>use_e2e_wired_table_rec_model</code> parameter of the pipeline object's <code>predict</code> method.</td>
+<td>No</td>
+</tr>
+<tr>
+<td><code>useE2eWirelessTableRecModel</code></td>
+<td><code>boolean</code></td>
+<td>Please refer to the description of the <code>use_e2e_wireless_table_rec_model</code> parameter of the pipeline object's <code>predict</code> method.</td>
+<td>No</td>
+</tr>
 </tbody>
 </table>
 <p>Each element in <code>tableRecResults</code> is an <code>object</code> with the following properties:</p>

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md

@@ -896,23 +896,23 @@ for res in output:
 </tr>
 <td><code>use_table_cells_ocr_results</code></td>
 <td>是否启用单元格OCR模式，不启用时采用全局OCR结果填充至HTML表格，启用时逐个单元格做OCR并填充至HTML表格（会增加耗时）。二者在不同场景下性能不同，请根据实际情况选择。</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> 或者 <code>False</code>
 <td><code>False</code></td>
 </tr>
 <td><code>use_e2e_wired_table_rec_model</code></td>
 <td>是否启用有线表格端到端预测模式，不启用时采用表格单元格检测模型预测结果填充至HTML表格，启用时采用端到端表格结构识别模型的单元格预测结果填充至HTML表格。二者在不同场景下性能不同，请根据实际情况选择。</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> 或者 <code>False</code>
 <td><code>False</code></td>
 </tr>
 <td><code>use_e2e_wireless_table_rec_model</code></td>
 <td>是否启用无线表格端到端预测模式，不启用时采用表格单元格检测模型预测结果填充至HTML表格，启用时采用端到端表格结构识别模型的单元格预测结果填充至HTML表格。二者在不同场景下性能不同，请根据实际情况选择。</td>
-<td><code>bool|False</code></td>
+<td><code>bool</code></td>
 <td>
 <ul>
 <li><b>bool</b>：<code>True</code> 或者 <code>False</code>
@@ -1326,6 +1326,24 @@ for res in output:
 <td>请参阅产线对象中 <code>predict</code> 方法的 <code>text_rec_score_thresh</code> 参数相关说明。</td>
 <td>否</td>
 </tr>
+<tr>
+<td><code>useTableCellsOcrResults</code></td>
+<td><code>boolean</code></td>
+<td>请参阅产线对象中 <code>predict</code> 方法的 <code>use_table_cells_ocr_results</code> 参数相关说明。</td>
+<td>否</td>
+</tr>
+<tr>
+<td><code>useE2eWiredTableRecModel</code></td>
+<td><code>boolean</code></td>
+<td>请参阅产线对象中 <code>predict</code> 方法的 <code>use_e2e_wired_table_rec_model</code> 参数相关说明。</td>
+<td>否</td>
+</tr>
+<tr>
+<td><code>useE2eWirelessTableRecModel</code></td>
+<td><code>boolean</code></td>
+<td>请参阅产线对象中 <code>predict</code> 方法的 <code>use_e2e_wireless_table_rec_model</code> 参数相关说明。</td>
+<td>否</td>
+</tr>
 </tbody>
 </table>
 <p><code>tableRecResults</code>中的每个元素为一个<code>object</code>，具有如下属性：</p>

paddlex/inference/pipelines/table_recognition/pipeline.py

@@ -217,7 +217,7 @@ def predict_doc_preprocessor_res(
             doc_preprocessor_res = {}
             doc_preprocessor_image = image_array
         return doc_preprocessor_res, doc_preprocessor_image
-    
+
     def split_ocr_bboxes_by_table_cells(self, ori_img, cells_bboxes):
         """
         Splits OCR bounding boxes by table cells and retrieves text.
@@ -241,7 +241,7 @@ def split_ocr_bboxes_by_table_cells(self, ori_img, cells_bboxes):
             # Perform OCR on the defined region of the image and get the recognized text.
             rec_te = next(self.general_ocr_pipeline(ori_img[y1:y2, x1:x2, :]))
             # Concatenate the texts and append them to the texts_list.
-            texts_list.append(''.join(rec_te["rec_texts"]))
+            texts_list.append("".join(rec_te["rec_texts"]))
         # Return the list of recognized texts from each cell.
         return texts_list
 
@@ -270,9 +270,15 @@ def predict_single_table_recognition_res(
         """
         table_structure_pred = next(self.table_structure_model(image_array))
         if use_table_cells_ocr_results == True:
-            table_cells_result = list(map(lambda arr: arr.tolist(), table_structure_pred['bbox']))
-            table_cells_result = [[rect[0], rect[1], rect[4], rect[5]] for rect in table_cells_result]
-            cells_texts_list = self.split_ocr_bboxes_by_table_cells(image_array, table_cells_result)
+            table_cells_result = list(
+                map(lambda arr: arr.tolist(), table_structure_pred["bbox"])
+            )
+            table_cells_result = [
+                [rect[0], rect[1], rect[4], rect[5]] for rect in table_cells_result
+            ]
+            cells_texts_list = self.split_ocr_bboxes_by_table_cells(
+                image_array, table_cells_result
+            )
         else:
             cells_texts_list = []
         single_table_recognition_res = get_table_recognition_res(
@@ -309,7 +315,7 @@ def predict(
         text_det_box_thresh: Optional[float] = None,
         text_det_unclip_ratio: Optional[float] = None,
         text_rec_score_thresh: Optional[float] = None,
-        use_table_cells_ocr_results: Optional[bool] = False,
+        use_table_cells_ocr_results: bool = False,
         cell_sort_by_y_projection: Optional[bool] = None,
         **kwargs,
     ) -> TableRecognitionResult: