What (and how) should I use for object detection in this case?
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I have read a few papers on object detection and analyzed several implementations of object detection applied to car number plates.
In my case, the task is a bit more difficult in that I need to detect a number (you might have seen the way containers are labeled using a sequence of letters and digits). Unlike car number plates, there is no unified form of the number plate, its location might differ etc.
The goal is to do object detection and then recognize (OCR) the code (as I said, this is a sequence of digits and letters. Good news this sequence is standard).
I have a few questions:
1) The first task is to perform object localization i.e. create a training dataset of annotated images ($x_{min}, y_{min},x_{max},y_{max}$, height,width).
There is an issue, however. Usually, this is not the only area on containers with digits and letters. Will the neural network be able to learn the right location?
2) Do I need to manually segment all digits and letters inside the "bounding box"? Or how should this stage be performed?
3) Is there any hope that Tesseract could be helpful for OCR? As far as I know, it's not very accurate for photos. If not, what is the best algorithm to do OCR after object detection?
4) Considering that I have almost no experience, would it be easier to start with Tensorflow Object Detection API or Yolo for object detection?
Below, I am attaching an example of the number which should be detected and recognized (BMOU 441929 7)
deep-learning cnn object-detection object-recognition ocr
asked 2 days ago
Don DraperDon Draper
1
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add a comment |
$begingroup$
I have read a few papers on object detection and analyzed several implementations of object detection applied to car number plates.
In my case, the task is a bit more difficult in that I need to detect a number (you might have seen the way containers are labeled using a sequence of letters and digits). Unlike car number plates, there is no unified form of the number plate, its location might differ etc.
The goal is to do object detection and then recognize (OCR) the code (as I said, this is a sequence of digits and letters. Good news this sequence is standard).
I have a few questions:
1) The first task is to perform object localization i.e. create a training dataset of annotated images ($x_{min}, y_{min},x_{max},y_{max}$, height,width).
There is an issue, however. Usually, this is not the only area on containers with digits and letters. Will the neural network be able to learn the right location?
2) Do I need to manually segment all digits and letters inside the "bounding box"? Or how should this stage be performed?
3) Is there any hope that Tesseract could be helpful for OCR? As far as I know, it's not very accurate for photos. If not, what is the best algorithm to do OCR after object detection?
4) Considering that I have almost no experience, would it be easier to start with Tensorflow Object Detection API or Yolo for object detection?
Below, I am attaching an example of the number which should be detected and recognized (BMOU 441929 7)
deep-learning cnn object-detection object-recognition ocr
asked 2 days ago
Don DraperDon Draper
1
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have read a few papers on object detection and analyzed several implementations of object detection applied to car number plates.
In my case, the task is a bit more difficult in that I need to detect a number (you might have seen the way containers are labeled using a sequence of letters and digits). Unlike car number plates, there is no unified form of the number plate, its location might differ etc.
The goal is to do object detection and then recognize (OCR) the code (as I said, this is a sequence of digits and letters. Good news this sequence is standard).
I have a few questions:
1) The first task is to perform object localization i.e. create a training dataset of annotated images ($x_{min}, y_{min},x_{max},y_{max}$, height,width).
There is an issue, however. Usually, this is not the only area on containers with digits and letters. Will the neural network be able to learn the right location?
2) Do I need to manually segment all digits and letters inside the "bounding box"? Or how should this stage be performed?
3) Is there any hope that Tesseract could be helpful for OCR? As far as I know, it's not very accurate for photos. If not, what is the best algorithm to do OCR after object detection?
4) Considering that I have almost no experience, would it be easier to start with Tensorflow Object Detection API or Yolo for object detection?
Below, I am attaching an example of the number which should be detected and recognized (BMOU 441929 7)
deep-learning cnn object-detection object-recognition ocr
asked 2 days ago
Don DraperDon Draper
1
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have read a few papers on object detection and analyzed several implementations of object detection applied to car number plates.
In my case, the task is a bit more difficult in that I need to detect a number (you might have seen the way containers are labeled using a sequence of letters and digits). Unlike car number plates, there is no unified form of the number plate, its location might differ etc.
The goal is to do object detection and then recognize (OCR) the code (as I said, this is a sequence of digits and letters. Good news this sequence is standard).
I have a few questions:
1) The first task is to perform object localization i.e. create a training dataset of annotated images ($x_{min}, y_{min},x_{max},y_{max}$, height,width).
There is an issue, however. Usually, this is not the only area on containers with digits and letters. Will the neural network be able to learn the right location?
2) Do I need to manually segment all digits and letters inside the "bounding box"? Or how should this stage be performed?
3) Is there any hope that Tesseract could be helpful for OCR? As far as I know, it's not very accurate for photos. If not, what is the best algorithm to do OCR after object detection?
4) Considering that I have almost no experience, would it be easier to start with Tensorflow Object Detection API or Yolo for object detection?
Below, I am attaching an example of the number which should be detected and recognized (BMOU 441929 7)
deep-learning cnn object-detection object-recognition ocr
deep-learning cnn object-detection object-recognition ocr
asked 2 days ago
Don DraperDon Draper
1
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Don DraperDon Draper
1
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Don DraperDon Draper
1
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Don DraperDon Draper
1
asked 2 days ago
Don DraperDon Draper
1
1
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Don Draper is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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