diff --git a/README.md b/README.md
index 4fa721d9f67b1f4c8ef7581b9ecc8cd543f3690c..f34c81bca872a212923db2aef6084acd46f442f6 100644
--- a/README.md
+++ b/README.md
@@ -102,7 +102,7 @@ For a new language request, please refer to [Guideline for new language_requests
 - PP-OCR Industry Landing: from Training to Deployment
     - [PP-OCR Model Zoo](./doc/doc_en/models_en.md)
         - [PP-OCR Model Download](./doc/doc_en/models_list_en.md)
-        - [Python Inference for PP-OCR Model Library](./doc/doc_en/inference_ppocr_en.md)
+        - [Python Inference for PP-OCR Model Zoo](./doc/doc_en/inference_ppocr_en.md)
     - [PP-OCR Training](./doc/doc_en/training_en.md)
         - [Text Detection](./doc/doc_en/detection_en.md)
         - [Text Recognition](./doc/doc_en/recognition_en.md)
diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md
index fa3b1c88713f01e8e411cf95d107b4b58dd7f4e1..056ce400c3f7245cebdd616c4a663bbee111fbbe 100755
--- a/doc/doc_en/inference_ppocr_en.md
+++ b/doc/doc_en/inference_ppocr_en.md
@@ -1,5 +1,5 @@
 
-# Python Inference for PP-OCR Model Library
+# Python Inference for PP-OCR Model Zoo
 
 This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
 
diff --git a/doc/doc_en/models_en.md b/doc/doc_en/models_en.md
index b7931b1868b042cfb880a8587add872438668fc0..7226f76498eddcb42bc41d63250d12b6f46f94c8 100644
--- a/doc/doc_en/models_en.md
+++ b/doc/doc_en/models_en.md
@@ -1,14 +1,14 @@
 # PP-OCR Model Zoo
 The PP-OCR model zoo section explains some basic concepts of the OCR model and how to quickly use the models in the PP-OCR model library.
 
-This section contains two parts. Firstly, [PP-OCR Model Download](. /models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models.  The next [Python Inference for PP-OCR Model Library](. /inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
+This section contains two parts. Firstly, [PP-OCR Model Download](./models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models.  The next [Python Inference for PP-OCR Model Zoo](./inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
 
 ------
 
 Let's first understand some basic concepts.
 
 - [INTRODUCTION ABOUT OCR](#introduction-about-ocr)
-  * [BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
+  * [Basic concepts of OCR detection model](#basic-concepts-of-ocr-detection-model)
   * [Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
   * [PP-OCR model](#pp-ocr-model)
   * [And a table of contents](#and-a-table-of-contents)
@@ -24,7 +24,7 @@ OCR (Optical Character Recognition, Optical Character Recognition) is currently
 OCR text recognition generally includes two parts, text detection and text recognition. The text detection module first uses detection algorithms to detect text lines in the image. And then the recognition algorithm to identify the specific text in the text line.
 
 
-### 1.1 BASIC CONCEPTS OF OCR DETECTION MODEL
+### 1.1 Basic concepts of OCR detection model
 
 Text detection can locate the text area in the image, and then usually mark the word or text line in the form of a bounding box. Traditional text detection algorithms mostly extract features manually, which are characterized by fast speed and good effect in simple scenes, but the effect will be greatly reduced when faced with natural scenes. Currently, deep learning methods are mostly used.