[Enhancement][Core] New approaches for Page/Template Alignment

[Udayraj123]

The template alignment problem is one of the central problems which currently remains unsolved for a general OMR sheet. This issue tracks various issues that we have noticed over a period of time in various samples.

Shifts during scanning

Despite fixing page curvature, we may still face the template alignment problem. Sometimes a scanner may malfunction at minute levels, we have seen uneven stretching of the page when using a basic scanner. The boxes need to be close to the bubbles otherwise the output is not reliable for any OMR checking software.
Example:

Curvature during bulk scanning

Sometimes the scan itself seems to be curved. we can see that the instructions text is not in a straight horizontal line.
Example:

The scans need to be straightened before applying template detection.

PDF-printed vs Photocopied misalignment

We have also seen shifts upto 10 pixels between scans of pdf-printed vs photocopied sheets. Link to an explainer: Google Drive

Describe the solution you'd like
Rough notes (ref):

- **Get all control-destination points **
    - From SIFT: 
        - Filter list: Slide a 10px(windowSize) window on the image and pick the point with max score
        - Filter list: pick the points with reasonable shifts(maxDisplacement)
    - This way we can scale to any set of feature detectors like ORB or custom detectors from the ScanZones. Just need a set of control-destination pairs.

- **Align the points **
    - Approach 1: 
        - Loop over each field, find 4 nearest anchor points and
        - apply the transform on the field box using findHomography
    - Approach 2: 
        - Triangulate the whole image based on the anchor points and 
        - perform affine transform on each triangle(all pixels get transformed)

Describe alternatives you've considered
There are many other approaches that can be tried:

• cv2.findTransformECC (Enhanced Correlation Coefficient Maximization)
• Combine SIFT with ORB
• Using appropriate loss functions to use in the Visual Attention based approach
• Spatial Transformation Networks, link2
• cv2.calcOpticalFlowFarneback
• Thin Plate Spline (implemented in this ImageJ plugin)
• We can also try to apply template matching over the "margin box" of a field-label? Hard to code, can't be sure if it covers all cases. In marked bubbles case: Still score might get max in the aligned. There's also the case of current question overflowing in the next question.

Additional context
NA