diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index cc580a69f741ae436fcc65f546083d311947b12c..9761ddbad9123372d706db158aba8008956f30e9 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -122,6 +122,8 @@ class TextRecognizer(object):
                     blank = probs.shape[1]
                     valid_ind = np.where(ind != (blank - 1))[0]
                     score = np.mean(probs[valid_ind, ind[valid_ind]])
+                    if len(valid_ind) == 0:
+                        continue
                     # rec_res.append([preds_text, score])
                     rec_res[indices[beg_img_no + rno]] = [preds_text, score]
             else:
diff --git a/tools/infer_rec.py b/tools/infer_rec.py
index b1ddc8dda198b92414bc7d7a853de78527456e5a..9abbf076e9a4902cdd0876c320021fc45e227a2c 100755
--- a/tools/infer_rec.py
+++ b/tools/infer_rec.py
@@ -99,6 +99,8 @@ def main():
             ind = np.argmax(probs, axis=1)
             blank = probs.shape[1]
             valid_ind = np.where(ind != (blank - 1))[0]
+            if len(valid_ind) == 0:
+                continue
             score = np.mean(probs[valid_ind, ind[valid_ind]])
         elif loss_type == "attention":
             preds = np.array(predict[0])
diff --git a/tools/train.py b/tools/train.py
index 287ed2059e9393a3fc1758e76b008891939a8424..15d6ebb2138ce19a2f65c7d1fabd56d86b7645be 100755
--- a/tools/train.py
+++ b/tools/train.py
@@ -36,7 +36,7 @@ set_paddle_flags(
     FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
 )
 
-import program
+import tools.program as program
 from paddle import fluid
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
@@ -106,6 +106,26 @@ def main():
         program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
 
 
+def test_reader():
+    config = program.load_config(FLAGS.config)
+    program.merge_config(FLAGS.opt)
+    print(config)
+    train_reader = reader_main(config=config, mode="train")
+    import time
+    starttime = time.time()
+    count = 0
+    try:
+        for data in train_reader():
+            count += 1
+            if count % 1 == 0:
+                batch_time = time.time() - starttime
+                starttime = time.time()
+                print("reader:", count, len(data), batch_time)
+    except Exception as e:
+        logger.info(e)
+    logger.info("finish reader: {}, Success!".format(count))
+
+
 if __name__ == '__main__':
     parser = program.ArgsParser()
     FLAGS = parser.parse_args()