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TODOs
Interesting tasks available in the whole range from Beginner to Advanced level. Looking for computer vision enthusiasts to take this project to the next level!
📝 Beginner Quests
🏆 Intermediate Challenges
🎓 Advanced Experiments
The tasks are ordered in increasing levels of difficulty below.
📝 Run code on your own images and add it to samples
📝 Generate your own code-in-action gifs on those images
📝 Use cv2.putText to add appropriate labels to all output images
📝 Add validation checks for configuration
📝 Implement Accuracy evaluation
There are 5-6 popular methods of evaluation available. We will be implementing all of them. See this link. From above link, "Exact match" method is implemented in main.py (look for 'TEST_FILE'). Need help in implementing any of the remaining methods. The ultimate plan is to create a reliable benchmark score for evaluating future algorithms. For any discussion/doubts, ask on discord.
📝 Contribute to Mobile images dataset using the Android App(Contact on discord)
📝 Add coloroma to output texts
📝 Product comparision articles: Review and Compare with Existing OMR Softwares with this free software. For a start, see Auto Multiple Choices
🏆 Generate Template Layout directly from blank OMR image.
Use methods like morphology and then blob detection to find presence of bubbles in a good quality image of a blank OMR Sheet like this one. Illustrative image coming soon.
🏆 Auto Rotating the OMR sheet
🏆 Calculate confidence using data
Count times when only localTHR was used vs globalTHR over localTHR. Image contrast levels score.
🏆 Extract code snippets which may be re-usable in other projects and add them to gist
🏆 Put explanatory text on output images
Show QBlock Labels and Column Orientations for template in the setLayout mode
🏆 Improve page boundary detection : Defeat the bossbg.jpg
🎓 Refactor code to a software design pattern
🎓 Faster Speeds : parallelization, pyrDowns, etc
🎓 Making more visualizations from available data. See Visualization Ideas for ideas on Flow diagrams, Animations and 3D outputs.
🎓 Explore methods to find global threshold more accurately(perhaps using ML)
🎓 Introduce git submodules