diff --git a/doc/simfang.ttf b/doc/simfang.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..2b59eae4195d1cdbea375503c0cc34d5631cb0f9
Binary files /dev/null and b/doc/simfang.ttf differ
diff --git a/tools/infer/predict_eval.py b/tools/infer/predict_eval.py
index 53ee7e2dcae3a5d3cffd0d445eedfa2b179eba49..af0a5e2715d081a5c1c55ce9ea84d1984735596a 100755
--- a/tools/infer/predict_eval.py
+++ b/tools/infer/predict_eval.py
@@ -22,28 +22,25 @@ import numpy as np
 import math
 import time
 import json
+import os
+from PIL import Image, ImageDraw, ImageFont
+from tools.infer.utility import draw_ocr
+from ppocr.utils.utility import get_image_file_list
 
 if __name__ == "__main__":
     args = utility.parse_args()
     text_sys = predict_system.TextSystem(args)
 
-    image_file_list = []
-    label_file_path = "./eval_perform/gt_res/test_chinese_ic15_500_4pts.txt"
-    img_set_path = "./eval_perform/"
-    with open(label_file_path, "rb") as fin:
-        lines = fin.readlines()
-        for line in lines:
-            substr = line.decode('utf-8').strip("\n").split("\t")
-            if "lsvt" in substr[0]:
-                continue
-            image_file_list.append(substr[0])
+    if not os.path.exists(args.image_dir):
+        raise Exception("{} not exists !!".format(args.image_dir))
+    image_file_list = get_image_file_list(args.image_dir)
 
     total_time_all = 0
     count = 0
-    save_path = "./output/predict.txt"
+    save_path = "./inference_output/predict.txt"
     fout = open(save_path, "wb")
     for image_name in image_file_list:
-        image_file = img_set_path + image_name
+        image_file = image_name
         img = cv2.imread(image_file)
         if img is None:
             logger.info("error in loading image:{}".format(image_file))
@@ -68,6 +65,20 @@ if __name__ == "__main__":
                 "points": points,
                 "scores": score * 1.0
             })
+        # draw predict box and text in image
+        # and save drawed image in save_path
+        image = Image.open(image_file)
+        boxes, txts, scores = [], [], []
+        for dic in bbox_list:
+            boxes.append(dic['points'])
+            txts.append(dic['transcription'])
+            scores.append(round(dic['scores'], 3))
+        new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
+        draw_img_save = os.path.join(
+            os.path.dirname(save_path), "inference_draw",
+            os.path.basename(image_file))
+        cv2.imwrite(draw_img_save, new_img)
+        # save predicted results in txt file
         otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
         fout.write(otstr.encode('utf-8'))
     avg_time = total_time_all / count
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index 6d56a99080c9fcc7dc5feade251956b28b965475..a8b0ebc001e0d9024ce7c131dc7ba376f333c626 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -21,6 +21,8 @@ from paddle.fluid.core import AnalysisConfig
 from paddle.fluid.core import create_paddle_predictor
 import cv2
 import numpy as np
+import json
+from PIL import Image, ImageDraw, ImageFont
 
 
 def parse_args():
@@ -108,3 +110,59 @@ def draw_text_det_res(dt_boxes, img_path):
         cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
     img_name_pure = img_path.split("/")[-1]
     cv2.imwrite("./output/%s" % img_name_pure, src_im)
+
+
+def draw_ocr(image, boxes, txts, scores, draw_txt):
+    from PIL import Image, ImageDraw, ImageFont
+
+    w, h = image.size
+    img = image.copy()
+    draw = ImageDraw.Draw(img)
+
+    for (box, txt) in zip(boxes, txts):
+
+        draw.line([(box[0][0], box[0][1]), (box[1][0], box[1][1])], fill='red')
+        draw.line([(box[1][0], box[1][1]), (box[2][0], box[2][1])], fill='red')
+        draw.line([(box[2][0], box[2][1]), (box[3][0], box[3][1])], fill='red')
+        draw.line([(box[3][0], box[3][1]), (box[0][0], box[0][1])], fill='red')
+
+    if draw_txt:
+        txt_color = (0, 0, 0)
+
+        blank_img = np.ones(shape=[h, 800], dtype=np.int8) * 255
+        blank_img = Image.fromarray(blank_img).convert("RGB")
+        draw_txt = ImageDraw.Draw(blank_img)
+
+        font_size = 30
+        gap = 40 if h // len(txts) >= font_size else h // len(txts)
+
+        for i, txt in enumerate(txts):
+            font = ImageFont.truetype(
+                "/simfang.TTF", font_size, encoding="utf-8")
+            new_txt = str(i) + ':  ' + txt + '    ' + str(scores[i])
+            draw_txt.text((20, gap * (i + 1)), new_txt, txt_color, font=font)
+
+        img = np.concatenate([np.array(img), np.array(blank_img)], axis=1)
+    return img
+
+
+if __name__ == '__main__':
+    test_img = "./doc/test_v2"
+    predict_txt = "./doc/predict.txt"
+    f = open(predict_txt, 'r')
+    data = f.readlines()
+    img_path, anno = data[0].strip().split('\t')
+    img_name = os.path.basename(img_path)
+    img_path = os.path.join(test_img, img_name)
+    image = Image.open(img_path)
+
+    data = json.loads(anno)
+    boxes, txts, scores = [], [], []
+    for dic in data:
+        boxes.append(dic['points'])
+        txts.append(dic['transcription'])
+        scores.append(round(dic['scores'], 3))
+
+    new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
+
+    cv2.imwrite(img_name, new_img)