Measure image similarity from a set
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0
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I'm trying to OCR some info from a document.
The input document can be one of some layouts, but all layouts are known.
I'll use CNN to detect what layout fits to the inputed document.
Once I know what's the document type I will align it to a mask, using image registration, and as I know the needed info position in the mask i'll use some OCR algorith in those cordinates in the inputed image to get my info out already labeled.
The problem is once I know the document type would be great to measure the most similar image from a set of images of the same type to be sure the image registration goes well. I've seen some topics trying to find a way to measure this distance but as all the documents are the same type, and so very similar, im afraid that hashing the image wont be precise, and using the number os points matched in image registration return me false positives.
Do you guys can advice me about the best way to find the most similar image from a set of similar images?
Is there a smarter way to aproach my problem?
I'm currently using python, opencv and tesseract.
python image opencv similarity measure
asked Nov 21 at 15:37
Rodrigo Faria
1
add a comment |
up vote
0
down vote
favorite
I'm trying to OCR some info from a document.
The input document can be one of some layouts, but all layouts are known.
I'll use CNN to detect what layout fits to the inputed document.
Once I know what's the document type I will align it to a mask, using image registration, and as I know the needed info position in the mask i'll use some OCR algorith in those cordinates in the inputed image to get my info out already labeled.
The problem is once I know the document type would be great to measure the most similar image from a set of images of the same type to be sure the image registration goes well. I've seen some topics trying to find a way to measure this distance but as all the documents are the same type, and so very similar, im afraid that hashing the image wont be precise, and using the number os points matched in image registration return me false positives.
Do you guys can advice me about the best way to find the most similar image from a set of similar images?
Is there a smarter way to aproach my problem?
I'm currently using python, opencv and tesseract.
python image opencv similarity measure
asked Nov 21 at 15:37
Rodrigo Faria
1
"similarity" is not a well defined concept. There are a lot of possibilities to compute some kind of similarity, like SSIM, PSNR, Sift Feature Matching, chamfer matching, bag of words and so on. But none of them will cover everything what a human (with or without donain knowledge) would call "similar".
– Micka
Nov 22 at 6:03
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I'm trying to OCR some info from a document.
The input document can be one of some layouts, but all layouts are known.
I'll use CNN to detect what layout fits to the inputed document.
Once I know what's the document type I will align it to a mask, using image registration, and as I know the needed info position in the mask i'll use some OCR algorith in those cordinates in the inputed image to get my info out already labeled.
The problem is once I know the document type would be great to measure the most similar image from a set of images of the same type to be sure the image registration goes well. I've seen some topics trying to find a way to measure this distance but as all the documents are the same type, and so very similar, im afraid that hashing the image wont be precise, and using the number os points matched in image registration return me false positives.
Do you guys can advice me about the best way to find the most similar image from a set of similar images?
Is there a smarter way to aproach my problem?
I'm currently using python, opencv and tesseract.
python image opencv similarity measure
asked Nov 21 at 15:37
Rodrigo Faria
1
I'm trying to OCR some info from a document.
The input document can be one of some layouts, but all layouts are known.
I'll use CNN to detect what layout fits to the inputed document.
Once I know what's the document type I will align it to a mask, using image registration, and as I know the needed info position in the mask i'll use some OCR algorith in those cordinates in the inputed image to get my info out already labeled.
The problem is once I know the document type would be great to measure the most similar image from a set of images of the same type to be sure the image registration goes well. I've seen some topics trying to find a way to measure this distance but as all the documents are the same type, and so very similar, im afraid that hashing the image wont be precise, and using the number os points matched in image registration return me false positives.
Do you guys can advice me about the best way to find the most similar image from a set of similar images?
Is there a smarter way to aproach my problem?
I'm currently using python, opencv and tesseract.
python image opencv similarity measure
python image opencv similarity measure
asked Nov 21 at 15:37
Rodrigo Faria
1
asked Nov 21 at 15:37
Rodrigo Faria
1
asked Nov 21 at 15:37
Rodrigo Faria
1
asked Nov 21 at 15:37
Rodrigo Faria
1
asked Nov 21 at 15:37
Rodrigo Faria
1
1
"similarity" is not a well defined concept. There are a lot of possibilities to compute some kind of similarity, like SSIM, PSNR, Sift Feature Matching, chamfer matching, bag of words and so on. But none of them will cover everything what a human (with or without donain knowledge) would call "similar".
– Micka
Nov 22 at 6:03
add a comment |
"similarity" is not a well defined concept. There are a lot of possibilities to compute some kind of similarity, like SSIM, PSNR, Sift Feature Matching, chamfer matching, bag of words and so on. But none of them will cover everything what a human (with or without donain knowledge) would call "similar".
– Micka
Nov 22 at 6:03
"similarity" is not a well defined concept. There are a lot of possibilities to compute some kind of similarity, like SSIM, PSNR, Sift Feature Matching, chamfer matching, bag of words and so on. But none of them will cover everything what a human (with or without donain knowledge) would call "similar".
– Micka
Nov 22 at 6:03
"similarity" is not a well defined concept. There are a lot of possibilities to compute some kind of similarity, like SSIM, PSNR, Sift Feature Matching, chamfer matching, bag of words and so on. But none of them will cover everything what a human (with or without donain knowledge) would call "similar".
– Micka
Nov 22 at 6:03
add a comment |
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"similarity" is not a well defined concept. There are a lot of possibilities to compute some kind of similarity, like SSIM, PSNR, Sift Feature Matching, chamfer matching, bag of words and so on. But none of them will cover everything what a human (with or without donain knowledge) would call "similar".
– Micka
Nov 22 at 6:03