diff --git a/tools/infer_kie.py b/tools/infer_kie.py
index 216df723d4a2366e9e236937bad7f856b12c148f..62ef697240ffe89fcb858c5308bd010105dde2ab 100755
--- a/tools/infer_kie.py
+++ b/tools/infer_kie.py
@@ -47,9 +47,9 @@ def read_class_list(filepath):
     return dict
 
 
-def draw_kie_result(batch, node, idx_to_cls):
-    img = batch[-2]
-    boxes = batch[-1]
+def draw_kie_result(batch, node, idx_to_cls, count):
+    img = batch[6].copy()
+    boxes = batch[7]
     h, w = img.shape[:2]
     pred_img = np.ones((h, w * 2, 3), dtype=np.uint8) * 255
     max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1)
@@ -77,11 +77,15 @@ def draw_kie_result(batch, node, idx_to_cls):
         text = pred_label + '(' + pred_score + ')'
         cv2.putText(pred_img, text, (x_min * 2, y_min),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
-
     vis_img = np.ones((h, w * 3, 3), dtype=np.uint8) * 255
     vis_img[:, :w] = img
     vis_img[:, w:] = pred_img
-    return vis_img
+    save_kie_path = os.path.dirname(config['Global']['save_res_path']) + "/kie_results/"
+    if not os.path.exists(save_kie_path):
+        os.makedirs(save_kie_path)
+    save_path = os.path.join(save_kie_path, str(count) + ".png")
+    cv2.imwrite(save_path, vis_img)
+    logger.info("The Kie Image saved in {}".format(save_path))
 
 
 def main():
@@ -89,7 +93,6 @@ def main():
 
     # build model
     model = build_model(config['Architecture'])
-
     init_model(config, model, logger)
 
     # create data ops
@@ -97,6 +100,8 @@ def main():
     for op in config['Eval']['dataset']['transforms']:
         transforms.append(op)
 
+    data_dir = config['Eval']['dataset']['data_dir']
+
     ops = create_operators(transforms, global_config)
 
     save_res_path = config['Global']['save_res_path']
@@ -109,11 +114,10 @@ def main():
     with open(save_res_path, "wb") as fout:
         with open(config['Global']['infer_img'], "rb") as f:
             lines = f.readlines()
-            for data_line in lines:
+            for index, data_line in enumerate(lines):
                 data_line = data_line.decode('utf-8')
                 substr = data_line.strip("\n").split("\t")
-                img_path, label = "/Users/hongyongjie/project/PaddleOCR/train_data/wildreceipt/" + substr[
-                    0], substr[1]
+                img_path, label = data_dir + "/" + substr[0], substr[1]
                 data = {'img_path': img_path, 'label': label}
                 with open(data['img_path'], 'rb') as f:
                     img = f.read()
@@ -126,9 +130,7 @@ def main():
                             batch[i], axis=0))
                 node, edge = model(batch_pred)
                 node = F.softmax(node, -1)
-                img = draw_kie_result(batch, node, idx_to_cls)
-                cv2.imwrite('1.png', img)
-                exit()
+                draw_kie_result(batch, node, idx_to_cls, index)
     logger.info("success!")