diff --git a/ppocr/modeling/necks/rnn.py b/ppocr/modeling/necks/rnn.py
index 004ec5641c2c4c33037e5a9af38ad255331a67f5..582be7c43683cc00af825994d3c05b3ad79d0882 100644
--- a/ppocr/modeling/necks/rnn.py
+++ b/ppocr/modeling/necks/rnn.py
@@ -21,18 +21,6 @@ from paddle import nn
 from ppocr.modeling.heads.rec_ctc_head import get_para_bias_attr
 
 
-class EncoderWithReshape(nn.Layer):
-    def __init__(self, in_channels, **kwargs):
-        super().__init__()
-        self.out_channels = in_channels
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        x = x.reshape((B, C, -1))
-        x = x.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
-        return x
-
-
 class Im2Seq(nn.Layer):
     def __init__(self, in_channels, **kwargs):
         super().__init__()
@@ -40,9 +28,8 @@ class Im2Seq(nn.Layer):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        assert H == 1
-        x = x.transpose((0, 2, 3, 1))
-        x = x.reshape((-1, C))
+        x = x.reshape((B, -1, W))
+        x = x.transpose((0, 2, 1))  # (NTC)(batch, width, channels)
         return x
 
 
@@ -50,49 +37,10 @@ class EncoderWithRNN(nn.Layer):
     def __init__(self, in_channels, hidden_size):
         super(EncoderWithRNN, self).__init__()
         self.out_channels = hidden_size * 2
-        # self.lstm1_fw = nn.LSTMCell(
-        #     in_channels,
-        #     hidden_size,
-        #     weight_ih_attr=ParamAttr(name='lstm_st1_fc1_w'),
-        #     bias_ih_attr=ParamAttr(name='lstm_st1_fc1_b'),
-        #     weight_hh_attr=ParamAttr(name='lstm_st1_out1_w'),
-        #     bias_hh_attr=ParamAttr(name='lstm_st1_out1_b'),
-        # )
-        # self.lstm1_bw = nn.LSTMCell(
-        #     in_channels,
-        #     hidden_size,
-        #     weight_ih_attr=ParamAttr(name='lstm_st1_fc2_w'),
-        #     bias_ih_attr=ParamAttr(name='lstm_st1_fc2_b'),
-        #     weight_hh_attr=ParamAttr(name='lstm_st1_out2_w'),
-        #     bias_hh_attr=ParamAttr(name='lstm_st1_out2_b'),
-        # )
-        # self.lstm2_fw = nn.LSTMCell(
-        #     hidden_size,
-        #     hidden_size,
-        #     weight_ih_attr=ParamAttr(name='lstm_st2_fc1_w'),
-        #     bias_ih_attr=ParamAttr(name='lstm_st2_fc1_b'),
-        #     weight_hh_attr=ParamAttr(name='lstm_st2_out1_w'),
-        #     bias_hh_attr=ParamAttr(name='lstm_st2_out1_b'),
-        # )
-        # self.lstm2_bw = nn.LSTMCell(
-        #     hidden_size,
-        #     hidden_size,
-        #     weight_ih_attr=ParamAttr(name='lstm_st2_fc2_w'),
-        #     bias_ih_attr=ParamAttr(name='lstm_st2_fc2_b'),
-        #     weight_hh_attr=ParamAttr(name='lstm_st2_out2_w'),
-        #     bias_hh_attr=ParamAttr(name='lstm_st2_out2_b'),
-        # )
         self.lstm = nn.LSTM(
             in_channels, hidden_size, direction='bidirectional', num_layers=2)
 
     def forward(self, x):
-        # fw_x, _ = self.lstm1_fw(x)
-        # fw_x, _ = self.lstm2_fw(fw_x)
-        #
-        # # bw
-        # bw_x, _ = self.lstm1_bw(x)
-        # bw_x, _ = self.lstm2_bw(bw_x)
-        # x = paddle.concat([fw_x, bw_x], axis=2)
         x, _ = self.lstm(x)
         return x
 
@@ -118,13 +66,13 @@ class EncoderWithFC(nn.Layer):
 class SequenceEncoder(nn.Layer):
     def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs):
         super(SequenceEncoder, self).__init__()
-        self.encoder_reshape = EncoderWithReshape(in_channels)
+        self.encoder_reshape = Im2Seq(in_channels)
         self.out_channels = self.encoder_reshape.out_channels
         if encoder_type == 'reshape':
             self.only_reshape = True
         else:
             support_encoder_dict = {
-                'reshape': EncoderWithReshape,
+                'reshape': Im2Seq,
                 'fc': EncoderWithFC,
                 'rnn': EncoderWithRNN
             }