How to use convolutional neural network on binary image using Keras?
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I am trying to train a cnn model for ocr using keras. I preprocessed the images by converting to grayscale, removing noise and then converting it to binary, as binary images work better in ocr. But the problem I am getting is that binary image has 2 dimensions and no channel dimension and conv2d in keras(well any conv layer in general) require 3 dimensions. So what should I do to add a dimension but keep image binary? I am using cv2 for image processing so please tell solutions using that preferably. Also tell me whether I am right that using binary image dataset is better for ocr.
python opencv image-processing keras conv-neural-network
asked Nov 9 at 9:51
Shantanu Shinde
57
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show 3 more comments
up vote
0
down vote
favorite
I am trying to train a cnn model for ocr using keras. I preprocessed the images by converting to grayscale, removing noise and then converting it to binary, as binary images work better in ocr. But the problem I am getting is that binary image has 2 dimensions and no channel dimension and conv2d in keras(well any conv layer in general) require 3 dimensions. So what should I do to add a dimension but keep image binary? I am using cv2 for image processing so please tell solutions using that preferably. Also tell me whether I am right that using binary image dataset is better for ocr.
python opencv image-processing keras conv-neural-network
asked Nov 9 at 9:51
Shantanu Shinde
57
change the dnn architecture to only use one channel. Or add redudant channels, but this will make your model unnecessarily complex.
– Micka
Nov 9 at 10:04
@Micka but the conv2d layer of keras requires 3 input dimensions. How can I change that? As for adding redundant channel how to add that?
– Shantanu Shinde
Nov 9 at 10:10
according to the docs:"When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last"."So I think you could useinput_shape=(height,width,1)for your grayscale or binary data? Sorry, from my side it is only theoretical. And I don't know how to duplicate channels or sth. in python.
– Micka
Nov 9 at 10:24
@Micka I am using binary, not grayscale
– Shantanu Shinde
Nov 9 at 10:54
yes, but it will be used as grayscale. The important thing is, that it is only 1 channel. That's the 1 ininput_shape=(height,width,1)
– Micka
Nov 9 at 11:01
|
show 3 more comments
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am trying to train a cnn model for ocr using keras. I preprocessed the images by converting to grayscale, removing noise and then converting it to binary, as binary images work better in ocr. But the problem I am getting is that binary image has 2 dimensions and no channel dimension and conv2d in keras(well any conv layer in general) require 3 dimensions. So what should I do to add a dimension but keep image binary? I am using cv2 for image processing so please tell solutions using that preferably. Also tell me whether I am right that using binary image dataset is better for ocr.
python opencv image-processing keras conv-neural-network
asked Nov 9 at 9:51
Shantanu Shinde
57
I am trying to train a cnn model for ocr using keras. I preprocessed the images by converting to grayscale, removing noise and then converting it to binary, as binary images work better in ocr. But the problem I am getting is that binary image has 2 dimensions and no channel dimension and conv2d in keras(well any conv layer in general) require 3 dimensions. So what should I do to add a dimension but keep image binary? I am using cv2 for image processing so please tell solutions using that preferably. Also tell me whether I am right that using binary image dataset is better for ocr.
python opencv image-processing keras conv-neural-network
python opencv image-processing keras conv-neural-network
asked Nov 9 at 9:51
Shantanu Shinde
57
asked Nov 9 at 9:51
Shantanu Shinde
57
asked Nov 9 at 9:51
Shantanu Shinde
57
asked Nov 9 at 9:51
Shantanu Shinde
57
asked Nov 9 at 9:51
Shantanu Shinde
57
57
change the dnn architecture to only use one channel. Or add redudant channels, but this will make your model unnecessarily complex.
– Micka
Nov 9 at 10:04
@Micka but the conv2d layer of keras requires 3 input dimensions. How can I change that? As for adding redundant channel how to add that?
– Shantanu Shinde
Nov 9 at 10:10
according to the docs:"When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last"."So I think you could useinput_shape=(height,width,1)for your grayscale or binary data? Sorry, from my side it is only theoretical. And I don't know how to duplicate channels or sth. in python.
– Micka
Nov 9 at 10:24
@Micka I am using binary, not grayscale
– Shantanu Shinde
Nov 9 at 10:54
yes, but it will be used as grayscale. The important thing is, that it is only 1 channel. That's the 1 ininput_shape=(height,width,1)
– Micka
Nov 9 at 11:01
|
show 3 more comments
change the dnn architecture to only use one channel. Or add redudant channels, but this will make your model unnecessarily complex.
– Micka
Nov 9 at 10:04
@Micka but the conv2d layer of keras requires 3 input dimensions. How can I change that? As for adding redundant channel how to add that?
– Shantanu Shinde
Nov 9 at 10:10
according to the docs:"When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last"."So I think you could useinput_shape=(height,width,1)for your grayscale or binary data? Sorry, from my side it is only theoretical. And I don't know how to duplicate channels or sth. in python.
– Micka
Nov 9 at 10:24
@Micka I am using binary, not grayscale
– Shantanu Shinde
Nov 9 at 10:54
yes, but it will be used as grayscale. The important thing is, that it is only 1 channel. That's the 1 ininput_shape=(height,width,1)
– Micka
Nov 9 at 11:01
change the dnn architecture to only use one channel. Or add redudant channels, but this will make your model unnecessarily complex.
– Micka
Nov 9 at 10:04
change the dnn architecture to only use one channel. Or add redudant channels, but this will make your model unnecessarily complex.
– Micka
Nov 9 at 10:04
@Micka but the conv2d layer of keras requires 3 input dimensions. How can I change that? As for adding redundant channel how to add that?
– Shantanu Shinde
Nov 9 at 10:10
@Micka but the conv2d layer of keras requires 3 input dimensions. How can I change that? As for adding redundant channel how to add that?
– Shantanu Shinde
Nov 9 at 10:10
according to the docs:
"When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last"." So I think you could use input_shape=(height,width,1) for your grayscale or binary data? Sorry, from my side it is only theoretical. And I don't know how to duplicate channels or sth. in python.– Micka
Nov 9 at 10:24
according to the docs:
"When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last"." So I think you could use input_shape=(height,width,1) for your grayscale or binary data? Sorry, from my side it is only theoretical. And I don't know how to duplicate channels or sth. in python.– Micka
Nov 9 at 10:24
@Micka I am using binary, not grayscale
– Shantanu Shinde
Nov 9 at 10:54
@Micka I am using binary, not grayscale
– Shantanu Shinde
Nov 9 at 10:54
yes, but it will be used as grayscale. The important thing is, that it is only 1 channel. That's the 1 in
input_shape=(height,width,1)– Micka
Nov 9 at 11:01
yes, but it will be used as grayscale. The important thing is, that it is only 1 channel. That's the 1 in
input_shape=(height,width,1)– Micka
Nov 9 at 11:01
|
show 3 more comments
1 Answer
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active
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0
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I got my solution. I used numpy function numpy.expand_dims() to add empty dimension. so it became (width,height,1). Here is what I did:-
img = np.expand_dims(img,axis=2)
answered Nov 9 at 14:45
Shantanu Shinde
57
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
I got my solution. I used numpy function numpy.expand_dims() to add empty dimension. so it became (width,height,1). Here is what I did:-
img = np.expand_dims(img,axis=2)
answered Nov 9 at 14:45
Shantanu Shinde
57
add a comment |
up vote
0
down vote
accepted
I got my solution. I used numpy function numpy.expand_dims() to add empty dimension. so it became (width,height,1). Here is what I did:-
img = np.expand_dims(img,axis=2)
answered Nov 9 at 14:45
Shantanu Shinde
57
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
I got my solution. I used numpy function numpy.expand_dims() to add empty dimension. so it became (width,height,1). Here is what I did:-
img = np.expand_dims(img,axis=2)
answered Nov 9 at 14:45
Shantanu Shinde
57
I got my solution. I used numpy function numpy.expand_dims() to add empty dimension. so it became (width,height,1). Here is what I did:-
img = np.expand_dims(img,axis=2)
answered Nov 9 at 14:45
Shantanu Shinde
57
answered Nov 9 at 14:45
Shantanu Shinde
57
answered Nov 9 at 14:45
Shantanu Shinde
57
answered Nov 9 at 14:45
Shantanu Shinde
57
57
add a comment |
add a comment |
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change the dnn architecture to only use one channel. Or add redudant channels, but this will make your model unnecessarily complex.
– Micka
Nov 9 at 10:04
@Micka but the conv2d layer of keras requires 3 input dimensions. How can I change that? As for adding redundant channel how to add that?
– Shantanu Shinde
Nov 9 at 10:10
according to the docs:
"When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the batch axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last"."So I think you could useinput_shape=(height,width,1)for your grayscale or binary data? Sorry, from my side it is only theoretical. And I don't know how to duplicate channels or sth. in python.– Micka
Nov 9 at 10:24
@Micka I am using binary, not grayscale
– Shantanu Shinde
Nov 9 at 10:54
yes, but it will be used as grayscale. The important thing is, that it is only 1 channel. That's the 1 in
input_shape=(height,width,1)– Micka
Nov 9 at 11:01