diff --git a/ppstructure/README.md b/ppstructure/README.md
index 8532bd0184f7e3e5ab4f6335e472da9b512d2c4b..fb3697bc1066262833ee20bcbb8f79833f264f14 100644
--- a/ppstructure/README.md
+++ b/ppstructure/README.md
@@ -19,7 +19,7 @@ The pipeline of PP-Structurev2 system is shown below. The document image first p
 - In the key information extraction task, the OCR engine is first used to extract the text content, and then the SER(semantic entity recognition) module obtains the semantic entities in the image, and finally the RE(relationship extraction) module obtains the correspondence between the semantic entities, thereby extracting the required key information.
 <img src="./docs/ppstructurev2_pipeline.png" width="100%"/>
 
-More technical details: 👉 [PP-Structurev2 Technical Report]()
+More technical details: 👉 [PP-Structurev2 Technical Report](docs/PP-Structurev2_introduction.md)
 
 PP-Structurev2 supports independent use or flexible collocation of each module. For example, you can use layout analysis alone or table recognition alone. Click the corresponding link below to get the tutorial for each independent module:
 
@@ -36,8 +36,8 @@ The main features of PP-Structurev2 are as follows:
 - Support structured table recognition, and output the final result to **Excel file**;
 - Support multimodal-based Key Information Extraction (KIE) tasks - **Semantic Entity Recognition** (SER) and **Relation Extraction (RE);
 - Support **layout recovery**, that is, restore the document in word or pdf format with the same layout as the original image;
-- Support customized training and multiple inference deployment methods such as python whl package quick use;
-- Connected with the semi-automatic data labeling tool PPOCRLabel, which supports the labeling of layout analysis, table recognition, and SER.
+- Support customized training and multiple inference deployment methods such as python whl package quick start;
+- Connect with the semi-automatic data labeling tool PPOCRLabel, which supports the labeling of layout analysis, table recognition, and SER.
 
 ## 3. Results
 
diff --git a/ppstructure/README_ch.md b/ppstructure/README_ch.md
index 11d20a90ca632bbaa1fc2c6720d7f9ade742d747..87a9c625b32c32e9c7fffb8ebc9b9fdf3b2130db 100644
--- a/ppstructure/README_ch.md
+++ b/ppstructure/README_ch.md
@@ -21,7 +21,7 @@ PP-Structurev2系统流程图如下所示，文档图像首先经过图像矫正
 - 关键信息抽取任务中，首先使用OCR引擎提取文本内容，然后由语义实体识别模块获取图像中的语义实体，最后经关系抽取模块获取语义实体之间的对应关系，从而提取需要的关键信息。
 <img src="./docs/ppstructurev2_pipeline.png" width="100%"/>
 
-更多技术细节：👉 [PP-Structurev2技术报告]()
+更多技术细节：👉 [PP-Structurev2技术报告](docs/PP-Structurev2_introduction.md)
 
 PP-Structurev2支持各个模块独立使用或灵活搭配，如，可以单独使用版面分析，或单独使用表格识别，点击下面相应链接获取各个独立模块的使用教程：