diff --git a/deploy/paddle2onnx/readme.md b/deploy/paddle2onnx/readme.md
index 782cffce9e5611b38da5bc002670a8282c08cc19..2148b3262d43f374a4310a5bfaaac854e92f9a32 100644
--- a/deploy/paddle2onnx/readme.md
+++ b/deploy/paddle2onnx/readme.md
@@ -18,8 +18,8 @@ python3.7 -m pip install paddle2onnx
 
 - 安装 ONNX
 ```
-# 建议安装 1.4.0 版本，可根据环境更换版本号
-python3.7 -m pip install onnxruntime==1.4.0
+# 建议安装 1.9.0 版本，可根据环境更换版本号
+python3.7 -m pip install onnxruntime==1.9.0
 ```
 
 ## 2. 模型转换
@@ -47,13 +47,15 @@ paddle2onnx --model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ \
 --params_filename=inference.pdiparams \
 --save_file=./inference/det_mobile_onnx/model.onnx \
 --opset_version=10 \
+--input_shape_dict="{'x': [-1, 3, -1, -1]}" \
 --enable_onnx_checker=True
 ```
 
 执行完毕后，ONNX 模型会被保存在 `./inference/det_mobile_onnx/` 路径下
 
-* 注意：以下几个模型暂不支持转换为 ONNX 模型：
-NRTR、SAR、RARE、SRN
+* 注意：对于OCR模型，转化过程中必须采用动态shape的形式，即加入选项--input_shape_dict="{'x': [-1, 3, -1, -1]}"，否则预测结果可能与直接使用Paddle预测有细微不同。
+  另外，以下几个模型暂不支持转换为 ONNX 模型：
+  NRTR、SAR、RARE、SRN
 
 ## 3. onnx 预测
 
@@ -72,5 +74,3 @@ root INFO: 1.jpg  [[[291, 295], [334, 292], [348, 844], [305, 847]], [[344, 296]
 The predict time of ../../doc/imgs/1.jpg: 0.06162881851196289
 The visualized image saved in ./inference_results/det_res_1.jpg
 ```
-
-* 注意：ONNX暂时不支持变长预测，需要将输入resize到固定输入，预测结果可能与直接使用Paddle预测有细微不同。
diff --git "a/notebook/notebook_ch/4.ppcor_system_strategy/PP-OCR\347\263\273\347\273\237\345\217\212\344\274\230\345\214\226\347\255\226\347\225\245.ipynb" "b/notebook/notebook_ch/4.ppocr_system_strategy/PP-OCR\347\263\273\347\273\237\345\217\212\344\274\230\345\214\226\347\255\226\347\225\245.ipynb"
similarity index 100%
rename from "notebook/notebook_ch/4.ppcor_system_strategy/PP-OCR\347\263\273\347\273\237\345\217\212\344\274\230\345\214\226\347\255\226\347\225\245.ipynb"
rename to "notebook/notebook_ch/4.ppocr_system_strategy/PP-OCR\347\263\273\347\273\237\345\217\212\344\274\230\345\214\226\347\255\226\347\225\245.ipynb"
diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py
index fc14fdbcf13a61b591d9ea6c2535aefe6e437ec6..f83255b732f5990de6a99d1149bab77d682c85b3 100644
--- a/ppocr/data/imaug/label_ops.py
+++ b/ppocr/data/imaug/label_ops.py
@@ -344,7 +344,7 @@ class KieLabelEncode(object):
         max_num = 300
         temp_bboxes = np.zeros([max_num, 4])
         h, _ = bboxes.shape
-        temp_bboxes[:h, :h] = bboxes
+        temp_bboxes[:h, :] = bboxes
 
         temp_relations = np.zeros([max_num, max_num, 5])
         temp_relations[:h, :h, :] = relations
diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py
index 5dfe8d648f06f6382e8e101a6002f7f1b7441323..95a099451bb12ef537e6f942df88da91df48038f 100755
--- a/tools/infer/predict_det.py
+++ b/tools/infer/predict_det.py
@@ -101,17 +101,22 @@ class TextDetector(object):
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
-        if self.use_onnx:
-            pre_process_list[0] = {
-                'DetResizeForTest': {
-                    'image_shape': [640, 640]
-                }
-            }
+
         self.preprocess_op = create_operators(pre_process_list)
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
             args, 'det', logger)
 
+        if self.use_onnx:
+            img_h, img_w = self.input_tensor.shape[2:]
+            if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
+                pre_process_list[0] = {
+                    'DetResizeForTest': {
+                        'image_shape': [img_h, img_w]
+                    }
+                }
+        self.preprocess_op = create_operators(pre_process_list)
+
         if args.benchmark:
             import auto_log
             pid = os.getpid()
diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index 7ec8eeadd3ac1232fc28287828a8383b258a2b3d..575e1925c9a84b1de2d98f14567623ada6dabcb8 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -109,7 +109,10 @@ class TextRecognizer(object):
         assert imgC == img.shape[2]
         imgW = int((32 * max_wh_ratio))
         if self.use_onnx:
-            imgW = 100
+            w = self.input_tensor.shape[3:][0]
+            if w is not None and w > 0:
+                imgW = w
+
         h, w = img.shape[:2]
         ratio = w / float(h)
         if math.ceil(imgH * ratio) > imgW: