该博客转自www.pyimagesearch.com，进行了相关修改补充。
Over the past few months I’ve gotten quite the number of requests landing in my inbox to build a bubble sheet/Scantron-like【简而言之答题卡】 test reader using computer vision and image processing techniques.
And while I’ve been having a lot of fun doing this series on machine learning and deep learning, I’d be lying if I said this little mini-project wasn’t a short, welcome break. One of my favorite parts of running the PyImageSearch blog is demonstrating how to build actual solutions to problems using computer vision.【显而易见，pyimageSearch正在向dl方向转型，实际上这也是我准备下一步要做的】
In fact, what makes this project so special is that we are going to combine the techniques from many previous blog posts, including building a document scanner, contour sorting, and perspective transforms. Using the knowledge gained from these previous posts, we’ll be able to make quick work of this bubble sheet scanner and test grader.
You see, last Friday afternoon I quickly Photoshopped an example bubble test paper, printed out a few copies, and then set to work on coding up the actual implementation.
Overall, I am quite pleased with this implementation and I think you’ll absolutely be able to use this bubble sheet grader/OMR system as a starting point for your own projects.【完成一个完整的项目仍然是非常有独特价值的】
Bubble sheet scanner and test grader using OMR, Python, and OpenCV
In the remainder of this blog post, I’ll discuss what exactly Optical Mark Recognition (OMR) is. I’ll then demonstrate how to implement a bubble sheet test scanner and grader using strictly computer vision and image processing techniques, along with the OpenCV library.
Once we have our OMR system implemented, I’ll provide sample results of our test grader on a few example exams, including ones that were filled out with nefarious intent.
Finally, I’ll discuss some of the shortcomings of this current bubble sheet scanner system and how we can improve it in future iterations.
What is Optical Mark Recognition (OMR)?【名称解释】
Optical Mark Recognition, or OMR for short, is the process of automatically analyzing human-marked documents and interpreting their results.【这个定义蛮好】
Arguably, the most famous, easily recognizable form of OMR are bubble sheet multiple choice tests, not unlike the ones you took in elementary school, middle school, or even high school.
If you’re unfamiliar with “bubble sheet tests” or the trademark/corporate name of “Scantron tests”, they are simply multiple-choice tests that you take as a student. Each question on the exam is a multiple choice — and you use a #2 pencil to mark the “bubble” that corresponds to the correct answer.【我在想，这个工作，可以和GOGPY一起，成为一个非常棒的新视频内容】
The most notable bubble sheet test you experienced (at least in the United States) were taking the SATs during high school, prior to filling out college admission applications.
I believe that the SATs use the software provided by Scantron to perform OMR and grade student exams, but I could easily be wrong there. I only make note of this because Scantron is used in over 98% of all US school districts.
In short, what I’m trying to say is that there is a massive market for Optical Mark Recognition and the ability to grade and interpret human-marked forms and exams.
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Over the past few months I’ve gotten quite the number of requests landing in my inbox to build a bubble sheet/Scantron-like【简而言之答题卡】 test reader using computer vision and image processing techniques.
And while I’ve been having a lot of fun doing this series on machine learning and deep learning, I’d be lying if I said this little mini-project wasn’t a short, welcome break. One of my favorite parts of running the PyImageSearch blog is demonstrating how to build actual solutions to problems using computer vision.【显而易见，pyimageSearch正在向dl方向转型，实际上这也是我准备下一步要做的】
In fact, what makes this project so special is that we are going to combine the techniques from many previous blog posts, including building a document scanner, contour sorting, and perspective transforms. Using the knowledge gained from these previous posts, we’ll be able to make quick work of this bubble sheet scanner and test grader.
You see, last Friday afternoon I quickly Photoshopped an example bubble test paper, printed out a few copies, and then set to work on coding up the actual implementation.
Overall, I am quite pleased with this implementation and I think you’ll absolutely be able to use this bubble sheet grader/OMR system as a starting point for your own projects.【完成一个完整的项目仍然是非常有独特价值的】
Bubble sheet scanner and test grader using OMR, Python, and OpenCV
In the remainder of this blog post, I’ll discuss what exactly Optical Mark Recognition (OMR) is. I’ll then demonstrate how to implement a bubble sheet test scanner and grader using strictly computer vision and image processing techniques, along with the OpenCV library.
Once we have our OMR system implemented, I’ll provide sample results of our test grader on a few example exams, including ones that were filled out with nefarious intent.
Finally, I’ll discuss some of the shortcomings of this current bubble sheet scanner system and how we can improve it in future iterations.
What is Optical Mark Recognition (OMR)?【名称解释】
Optical Mark Recognition, or OMR for short, is the process of automatically analyzing human-marked documents and interpreting their results.【这个定义蛮好】
Arguably, the most famous, easily recognizable form of OMR are bubble sheet multiple choice tests, not unlike the ones you took in elementary school, middle school, or even high school.
If you’re unfamiliar with “bubble sheet tests” or the trademark/corporate name of “Scantron tests”, they are simply multiple-choice tests that you take as a student. Each question on the exam is a multiple choice — and you use a #2 pencil to mark the “bubble” that corresponds to the correct answer.【我在想，这个工作，可以和GOGPY一起，成为一个非常棒的新视频内容】
The most notable bubble sheet test you experienced (at least in the United States) were taking the SATs during high school, prior to filling out college admission applications.
I believe that the SATs use the software provided by Scantron to perform OMR and grade student exams, but I could easily be wrong there. I only make note of this because Scantron is used in over 98% of all US school districts.
In short, what I’m trying to say is that there is a massive market for Optical Mark Recognition and the ability to grade and interpret human-marked forms and exams.