diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md
index f3f083d195b09805b2d16af1caef3408817a2970..a384e01176c6715ef2daa88f18aeffdf7b37a4d6 100644
--- a/deploy/pdserving/README_CN.md
+++ b/deploy/pdserving/README_CN.md
@@ -6,7 +6,6 @@ PaddleOCR提供2种服务部署方式：
 - 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../../deploy/hubserving/readme.md)；
 - 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。
 
-* AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](https://aistudio.baidu.com/aistudio/projectdetail/3630726)
 
 # 基于PaddleServing的服务部署
 
@@ -19,6 +18,8 @@ PaddleOCR提供2种服务部署方式：
 
 更多有关PaddleServing服务化部署框架介绍和使用教程参考[文档](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)。
 
+AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](https://aistudio.baidu.com/aistudio/projectdetail/3630726)。
+
 ## 目录
 - [环境准备](#环境准备)
 - [模型转换](#模型转换)
@@ -135,7 +136,7 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
     python3 pipeline_http_client.py
     ```
     成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：
-    ![](./imgs/results.png)
+    ![](./imgs/pipeline_result.png)
 
     调整 config.yml 中的并发个数获得最大的QPS, 一般检测和识别的并发数为2：1
     ```
diff --git a/deploy/pdserving/imgs/pipeline_result.png b/deploy/pdserving/imgs/pipeline_result.png
new file mode 100644
index 0000000000000000000000000000000000000000..1058daa9b2b47ea88b835d7b2b3c49f131833f39
Binary files /dev/null and b/deploy/pdserving/imgs/pipeline_result.png differ
diff --git a/deploy/pdserving/pipeline_http_client.py b/deploy/pdserving/pipeline_http_client.py
index 61d13178220118eaf53c51723a9ef65201373ffb..a0ade26c716900d6f220c0dc1f4d8f8795b2dc55 100644
--- a/deploy/pdserving/pipeline_http_client.py
+++ b/deploy/pdserving/pipeline_http_client.py
@@ -34,12 +34,16 @@ test_img_dir = args.image_dir
 for idx, img_file in enumerate(os.listdir(test_img_dir)):
     with open(os.path.join(test_img_dir, img_file), 'rb') as file:
         image_data1 = file.read()
+    print('{}{}{}'.format('*' * 10, img_file, '*' * 10))
 
     image = cv2_to_base64(image_data1)
 
-    for i in range(1):
-        data = {"key": ["image"], "value": [image]}
-        r = requests.post(url=url, data=json.dumps(data))
-        print(r.json())
+    data = {"key": ["image"], "value": [image]}
+    r = requests.post(url=url, data=json.dumps(data))
+    all_result = r.json()["value"][0]
+    for item in eval(all_result):
+        print(item)
+    #print("len result:", len(result))
+    #print(eval(result[0]))
 
 print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))