chore: add teds score & fix setup package

Author: Joker1212

README.md

@@ -23,6 +23,7 @@
     - 输入输出格式对齐RapidTable
     - 支持模型自动下载
     - 增加来自paddle的新表格分类模型
+    - 增加最新PaddleX表格识别模型测评值
 
 ### 简介
 💖该仓库是用来对文档中表格做结构化识别的推理库，包括来自阿里读光有线和无线表格识别模型，llaipython(微信)贡献的有线表格模型，网易Qanything内置表格分类模型等。\
@@ -56,18 +57,19 @@
    Surya-Tabled 使用内置ocr模块，表格模型为行列识别模型，无法识别单元格合并，导致分数较低
 
 | 方法                                                                                                       |    TEDS     | TEDS-only-structure |
-|:---------------------------------------------------------------------------------------------------------|:-----------:|:-------------------:|
-| [surya-tabled(--skip-detect)](https://github.com/VikParuchuri/tabled)                                    |   0.33437   |       0.65865       |
-| [surya-tabled](https://github.com/VikParuchuri/tabled)                                                   |   0.33940   |       0.67103       |
-| [deepdoctection(table-transformer)](https://github.com/deepdoctection/deepdoctection?tab=readme-ov-file) |   0.59975   |       0.69918       |
-| [ppstructure_table_master](https://github.com/PaddlePaddle/PaddleOCR/tree/main/ppstructure)              |   0.61606   |       0.73892       |
-| [ppsturcture_table_engine](https://github.com/PaddlePaddle/PaddleOCR/tree/main/ppstructure)              |   0.67924   |       0.78653       |
-| [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)                              |   0.67310   |       0.81210       |
-| [RapidTable(SLANet)](https://github.com/RapidAI/RapidTable)                                              |   0.71654   |       0.81067       |
-| table_cls + wired_table_rec v1 + lineless_table_rec                                                      |   0.75288   |       0.82574       |
-| table_cls + wired_table_rec v2 + lineless_table_rec                                                      |   0.77676   |       0.84580       |
-| [RapidTable(SLANet-plus)](https://github.com/RapidAI/RapidTable)                                         | 0.84481 |     0.91369     |
-| [RapidTable(unitable)](https://github.com/RapidAI/RapidTable)                                         | **0.86200** |     **0.91813**     |
+|:---------------------------------------------------------------------------------------------------------|:-----------:|:-----------------:|
+| [surya-tabled(--skip-detect)](https://github.com/VikParuchuri/tabled)                                    |   0.33437   |       0.65865     |
+| [surya-tabled](https://github.com/VikParuchuri/tabled)                                                   |   0.33940   |       0.67103     |
+| [deepdoctection(table-transformer)](https://github.com/deepdoctection/deepdoctection?tab=readme-ov-file) |   0.59975   |       0.69918     |
+| [ppstructure_table_master](https://github.com/PaddlePaddle/PaddleOCR/tree/main/ppstructure)              |   0.61606   |       0.73892     |
+| [ppsturcture_table_engine](https://github.com/PaddlePaddle/PaddleOCR/tree/main/ppstructure)              |   0.67924   |       0.78653     |
+| [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)                              |   0.67310   |       0.81210     |
+| [RapidTable(SLANet)](https://github.com/RapidAI/RapidTable)                                              |   0.71654   |       0.81067     |
+| table_cls + wired_table_rec v1 + lineless_table_rec                                                      |   0.75288   |       0.82574     |
+| table_cls + wired_table_rec v2 + lineless_table_rec                                                      |   0.77676   |       0.84580     |
+| [PaddleX(SLANetXt+RT-DERT)](https://github.com/PaddlePaddle/PaddleX)                                                                            |   0.79900   |       **0.92222**     |
+| [RapidTable(SLANet-plus)](https://github.com/RapidAI/RapidTable)                                         |   0.84481   |       0.91369     |
+| [RapidTable(unitable)](https://github.com/RapidAI/RapidTable)                                            | **0.86200** |     0.91813     |
 
 ### 使用建议
 wired_table_rec_v2(有线表格精度最高): 通用场景有线表格(论文，杂志，期刊, 收据，单据，账单)

README_en.md

@@ -67,7 +67,10 @@ Surya-Tabled uses its built-in OCR module, which is a row-column recognition mod
 | [RapidTable(SLANet)](https://github.com/RapidAI/RapidTable)                                     |   0.71654   |       0.81067       |
 | table_cls + wired_table_rec v1 + lineless_table_rec                                             |   0.75288   |       0.82574       |
 | table_cls + wired_table_rec v2 + lineless_table_rec                                             |   0.77676   |       0.84580       |
+| [PaddleX(SLANetXt+RT-DERT)](https://github.com/PaddlePaddle/PaddleX)                                                                            |   0.79900   |       **0.92222**     |
 | [RapidTable(SLANet-plus)](https://github.com/RapidAI/RapidTable)                                | **0.84481** |     **0.91369**     |
+| [RapidTable(unitable)](https://github.com/RapidAI/RapidTable)                                            | **0.86200** |     0.91813     |
+
 
 ### Usage Recommendations
 wired_table_rec_v2 (highest precision for wired tables): General scenes for wired tables (papers, magazines, journals, receipts, invoices, bills)

setup_lineless.py

@@ -53,7 +53,7 @@ def read_txt(txt_path: Union[Path, str]) -> List[str]:
     license="Apache-2.0",
     install_requires=read_txt("requirements.txt"),
     include_package_data=True,
-    packages=[MODULE_NAME],
+    packages=[MODULE_NAME, f"{MODULE_NAME}.utils"],
     keywords=["tsr,ocr,table-recognition"],
     classifiers=[
         "Programming Language :: Python :: 3.6",

setup_table_cls.py

@@ -46,7 +46,7 @@ def read_txt(txt_path: Union[Path, str]) -> List[str]:
     license="Apache-2.0",
     install_requires=read_txt("requirements.txt"),
     include_package_data=True,
-    packages=[MODULE_NAME],
+    packages=[MODULE_NAME, f"{MODULE_NAME}.utils"],
     keywords=["table-classifier", "wired", "wireless", "table-recognition"],
     classifiers=[
         "Programming Language :: Python :: 3.6",

setup_wired.py

@@ -53,7 +53,7 @@ def read_txt(txt_path: Union[Path, str]) -> List[str]:
     license="Apache-2.0",
     install_requires=read_txt("requirements.txt"),
     include_package_data=True,
-    packages=[MODULE_NAME],
+    packages=[MODULE_NAME, f"{MODULE_NAME}.utils"],
     keywords=["tsr,ocr,table-recognition"],
     classifiers=[
         "Programming Language :: Python :: 3.6",