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How to combine the results of multiple OCR tools to get better text recognition
How to get Indexing Service and MODI to produce Full-text over OCR?OCR combined with font recognition?JavaScript text recognition and OCR on <canvas>Text Recognition using ocr of MatlabHow to improve text recognition using Microsoft OCR?The OCR text recognition camera is not launchingFull Page Text Recognition Dataset CreationGetting better results with OCRFloor Plan Text Recognition & OCROCR text recognition
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Imagine, you have different OCR tools to read text from images but none of them gives you a 100% accurate output. Combined however, the result could come very close to the ground truth - What would be the best technique to "fuse" the text together to get good results?
Example:
Actual text
§ 5.1: The contractor is obliged to announce the delay by 01.01.2019 at the latest. The identification-number to be used is OZ-771LS.
OCR tool 1
5 5.1 The contractor is obliged to announce the delay by O1.O1.2019 at the latest. The identification-number to be used is OZ77lLS.
OCR tool 2
§5.1: The contract or is obliged to announce theedelay by 01.O1. 2O19 at the latest. The identification number to be used is O7-771LS
OCR tool 3
§ 5.1: The contractor is oblige to do announced he delay by 01.01.2019 at the latest. T he identification-number ti be used is OZ-771LS.
What could be a promising algorithm to fuse OCR 1, 2 and 3 to get the actual text?
My first idea was creating a "tumbling window" of an arbitrary length, compare the words in the window and take the words 2 out of 3 tools predict for every position.
For example with window size 3:
[5 5.1 The]
[§5.1: The contract]
[§ 5.1: The]
As you see, the algorithm doesn't work as all three tools have different candidates for position one (5, §5.1: and §).
Of course it would be possible to add some tricks like Levenshtein distance to allow some deviations but I fear this will not really be robust enough.
nlp computer-vision ocr sensor-fusion
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
add a comment |
Imagine, you have different OCR tools to read text from images but none of them gives you a 100% accurate output. Combined however, the result could come very close to the ground truth - What would be the best technique to "fuse" the text together to get good results?
Example:
Actual text
§ 5.1: The contractor is obliged to announce the delay by 01.01.2019 at the latest. The identification-number to be used is OZ-771LS.
OCR tool 1
5 5.1 The contractor is obliged to announce the delay by O1.O1.2019 at the latest. The identification-number to be used is OZ77lLS.
OCR tool 2
§5.1: The contract or is obliged to announce theedelay by 01.O1. 2O19 at the latest. The identification number to be used is O7-771LS
OCR tool 3
§ 5.1: The contractor is oblige to do announced he delay by 01.01.2019 at the latest. T he identification-number ti be used is OZ-771LS.
What could be a promising algorithm to fuse OCR 1, 2 and 3 to get the actual text?
My first idea was creating a "tumbling window" of an arbitrary length, compare the words in the window and take the words 2 out of 3 tools predict for every position.
For example with window size 3:
[5 5.1 The]
[§5.1: The contract]
[§ 5.1: The]
As you see, the algorithm doesn't work as all three tools have different candidates for position one (5, §5.1: and §).
Of course it would be possible to add some tricks like Levenshtein distance to allow some deviations but I fear this will not really be robust enough.
nlp computer-vision ocr sensor-fusion
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
Might be helpful to view this as a merging problem. Not a trivial topic, though.
– afarley
Mar 26 at 23:45
add a comment |
Imagine, you have different OCR tools to read text from images but none of them gives you a 100% accurate output. Combined however, the result could come very close to the ground truth - What would be the best technique to "fuse" the text together to get good results?
Example:
Actual text
§ 5.1: The contractor is obliged to announce the delay by 01.01.2019 at the latest. The identification-number to be used is OZ-771LS.
OCR tool 1
5 5.1 The contractor is obliged to announce the delay by O1.O1.2019 at the latest. The identification-number to be used is OZ77lLS.
OCR tool 2
§5.1: The contract or is obliged to announce theedelay by 01.O1. 2O19 at the latest. The identification number to be used is O7-771LS
OCR tool 3
§ 5.1: The contractor is oblige to do announced he delay by 01.01.2019 at the latest. T he identification-number ti be used is OZ-771LS.
What could be a promising algorithm to fuse OCR 1, 2 and 3 to get the actual text?
My first idea was creating a "tumbling window" of an arbitrary length, compare the words in the window and take the words 2 out of 3 tools predict for every position.
For example with window size 3:
[5 5.1 The]
[§5.1: The contract]
[§ 5.1: The]
As you see, the algorithm doesn't work as all three tools have different candidates for position one (5, §5.1: and §).
Of course it would be possible to add some tricks like Levenshtein distance to allow some deviations but I fear this will not really be robust enough.
nlp computer-vision ocr sensor-fusion
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
Imagine, you have different OCR tools to read text from images but none of them gives you a 100% accurate output. Combined however, the result could come very close to the ground truth - What would be the best technique to "fuse" the text together to get good results?
Example:
Actual text
§ 5.1: The contractor is obliged to announce the delay by 01.01.2019 at the latest. The identification-number to be used is OZ-771LS.
OCR tool 1
5 5.1 The contractor is obliged to announce the delay by O1.O1.2019 at the latest. The identification-number to be used is OZ77lLS.
OCR tool 2
§5.1: The contract or is obliged to announce theedelay by 01.O1. 2O19 at the latest. The identification number to be used is O7-771LS
OCR tool 3
§ 5.1: The contractor is oblige to do announced he delay by 01.01.2019 at the latest. T he identification-number ti be used is OZ-771LS.
What could be a promising algorithm to fuse OCR 1, 2 and 3 to get the actual text?
My first idea was creating a "tumbling window" of an arbitrary length, compare the words in the window and take the words 2 out of 3 tools predict for every position.
For example with window size 3:
[5 5.1 The]
[§5.1: The contract]
[§ 5.1: The]
As you see, the algorithm doesn't work as all three tools have different candidates for position one (5, §5.1: and §).
Of course it would be possible to add some tricks like Levenshtein distance to allow some deviations but I fear this will not really be robust enough.
nlp computer-vision ocr sensor-fusion
nlp computer-vision ocr sensor-fusion
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
edited Mar 26 at 23:37
Simon Nobel
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
asked Mar 26 at 23:28
Simon NobelSimon Nobel
1071 silver badge11 bronze badges
1071 silver badge11 bronze badges
Might be helpful to view this as a merging problem. Not a trivial topic, though.
– afarley
Mar 26 at 23:45
add a comment |
Might be helpful to view this as a merging problem. Not a trivial topic, though.
– afarley
Mar 26 at 23:45
Might be helpful to view this as a merging problem. Not a trivial topic, though.
– afarley
Mar 26 at 23:45
Might be helpful to view this as a merging problem. Not a trivial topic, though.
– afarley
Mar 26 at 23:45
add a comment |
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Might be helpful to view this as a merging problem. Not a trivial topic, though.
– afarley
Mar 26 at 23:45