Removing noise from Handwritten text
Is there any way to remove the noise from the below picture? Like I want the horizontal and vertical lines, small already printed texts removed except the handwritten text.
I want the handwritten text to be extracted like in this image.
Is there any wayout?
I was trying Python OpenCV's library to threshold the noise pixels out of the image but that is not giving perfect result.
The image I'm getting after using threshold is this.
If I increase the threshold then it is removing pixels from the handwritten text itself.
python python-3.x image-processing machine-learning opencv3.0
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
add a comment |
Is there any way to remove the noise from the below picture? Like I want the horizontal and vertical lines, small already printed texts removed except the handwritten text.
I want the handwritten text to be extracted like in this image.
Is there any wayout?
I was trying Python OpenCV's library to threshold the noise pixels out of the image but that is not giving perfect result.
The image I'm getting after using threshold is this.
If I increase the threshold then it is removing pixels from the handwritten text itself.
python python-3.x image-processing machine-learning opencv3.0
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
2
Perhaps if you show what you have tried someone might be able to suggest an improvement.
– khelwood
Nov 16 '18 at 8:54
okay added the reference image.
– Desmond
Nov 16 '18 at 9:04
add a comment |
Is there any way to remove the noise from the below picture? Like I want the horizontal and vertical lines, small already printed texts removed except the handwritten text.
I want the handwritten text to be extracted like in this image.
Is there any wayout?
I was trying Python OpenCV's library to threshold the noise pixels out of the image but that is not giving perfect result.
The image I'm getting after using threshold is this.
If I increase the threshold then it is removing pixels from the handwritten text itself.
python python-3.x image-processing machine-learning opencv3.0
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
Is there any way to remove the noise from the below picture? Like I want the horizontal and vertical lines, small already printed texts removed except the handwritten text.
I want the handwritten text to be extracted like in this image.
Is there any wayout?
I was trying Python OpenCV's library to threshold the noise pixels out of the image but that is not giving perfect result.
The image I'm getting after using threshold is this.
If I increase the threshold then it is removing pixels from the handwritten text itself.
python python-3.x image-processing machine-learning opencv3.0
python python-3.x image-processing machine-learning opencv3.0
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
edited Nov 16 '18 at 9:03
Desmond
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
asked Nov 16 '18 at 8:52
DesmondDesmond
5518
5518
2
Perhaps if you show what you have tried someone might be able to suggest an improvement.
– khelwood
Nov 16 '18 at 8:54
okay added the reference image.
– Desmond
Nov 16 '18 at 9:04
add a comment |
2
Perhaps if you show what you have tried someone might be able to suggest an improvement.
– khelwood
Nov 16 '18 at 8:54
okay added the reference image.
– Desmond
Nov 16 '18 at 9:04
2
2
Perhaps if you show what you have tried someone might be able to suggest an improvement.
– khelwood
Nov 16 '18 at 8:54
Perhaps if you show what you have tried someone might be able to suggest an improvement.
– khelwood
Nov 16 '18 at 8:54
okay added the reference image.
– Desmond
Nov 16 '18 at 9:04
okay added the reference image.
– Desmond
Nov 16 '18 at 9:04
add a comment |
2 Answers
2
active
oldest
votes
Combine the OpenCV methods erode and dilate. They are ablte to first (erode) get rid of noise and then amplify the main signale (dilate).
answered Nov 16 '18 at 9:12
AussAuss
705
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
add a comment |
You can develop an algorithm based "connected component analysis" to remove undesired connected components. You just need to detect connected components and remove the small ones to extract the desired ones. A case study about it can be found in here and can be helpful for you to develop the solution for your case.
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Combine the OpenCV methods erode and dilate. They are ablte to first (erode) get rid of noise and then amplify the main signale (dilate).
answered Nov 16 '18 at 9:12
AussAuss
705
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
add a comment |
Combine the OpenCV methods erode and dilate. They are ablte to first (erode) get rid of noise and then amplify the main signale (dilate).
answered Nov 16 '18 at 9:12
AussAuss
705
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
add a comment |
Combine the OpenCV methods erode and dilate. They are ablte to first (erode) get rid of noise and then amplify the main signale (dilate).
answered Nov 16 '18 at 9:12
AussAuss
705
Combine the OpenCV methods erode and dilate. They are ablte to first (erode) get rid of noise and then amplify the main signale (dilate).
answered Nov 16 '18 at 9:12
AussAuss
705
answered Nov 16 '18 at 9:12
AussAuss
705
answered Nov 16 '18 at 9:12
AussAuss
705
answered Nov 16 '18 at 9:12
AussAuss
705
705
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
add a comment |
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
Thanks, this worked a little. Now noise is not totally removed yet, but yes comparatively less.
– Desmond
Nov 17 '18 at 15:03
add a comment |
You can develop an algorithm based "connected component analysis" to remove undesired connected components. You just need to detect connected components and remove the small ones to extract the desired ones. A case study about it can be found in here and can be helpful for you to develop the solution for your case.
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
add a comment |
You can develop an algorithm based "connected component analysis" to remove undesired connected components. You just need to detect connected components and remove the small ones to extract the desired ones. A case study about it can be found in here and can be helpful for you to develop the solution for your case.
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
add a comment |
You can develop an algorithm based "connected component analysis" to remove undesired connected components. You just need to detect connected components and remove the small ones to extract the desired ones. A case study about it can be found in here and can be helpful for you to develop the solution for your case.
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
You can develop an algorithm based "connected component analysis" to remove undesired connected components. You just need to detect connected components and remove the small ones to extract the desired ones. A case study about it can be found in here and can be helpful for you to develop the solution for your case.
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
answered Nov 16 '18 at 14:22
OzluOzlu
3601414
3601414
add a comment |
add a comment |
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2
Perhaps if you show what you have tried someone might be able to suggest an improvement.
– khelwood
Nov 16 '18 at 8:54
okay added the reference image.
– Desmond
Nov 16 '18 at 9:04