Extacting Regions of Interest using Page Markers

Categories Computer Vision, Uncategorized

Source on GitHub: https://github.com/ayoungprogrammer/OMR-Example


Optical Mark Recognition is recognizing certain “marks” on an image and using those marks as a reference point to extract other regions of interest (ROI) on the page. OMR is a relatively new technology and there is close to no documentation on the subject. Current OMR technologies like ScanTron require custom machines designed specifically to scan custom sheets of paper. These methods work well but the cost to produce the machines and paper is high as well as the inflexibility. Hopefully I can provide some insight into creating an efficient and effective OMR algorithm that uses standard household scanners and a simple template.

An OMR algorithm first needs a template page to know where ROI’s are in relation to the markers. It then needs to be able to scan a page and recognize where the markers are. Then using the template, the algorithm can determine where the ROI’s are in relation to the markers. In the case of ScanTrons, the markers are the black lines on the sides and ROI’s are the bubbles that are checked.

For an effective OMR, the markers should be at least halfway across the page from each other (either vertically or horizontally). The further apart the markers are, the higher accuracy you will achieve.

For the simplicity of this tutorial, we will use two QR codes with one in each corner as the markers. This will be our template:

Opening the template in Paint, we can find the coordinate of the ROI’s and markers.
Top right point of first QR code:
Bottom left point of second QR code:
Region of Interests (ROI’s)
Name box:
(223,105) -> (603,152)
Payroll # box:
(223,152)->(603, 198)
Sin box:
(223, 198)->(603,244)
Address box:
Postal box:
(223, 291)->(603,336)
(129,491) -> (766,806)

Using the coordinate we can do some simple math to find the relative positioning of the ROI’s.

We can also find the angle of rotation from the markers. If we find the angle between the top right corner and bottom left corner of the template markers we get: 53.48222 degrees. If we find that the markers we scan have is something different from that angle, we rotate the whole page by that angle, it will fix the skewed rotation.

Scanned image:

OMR Processed Image + Fixed rotation


Two QR Codes in each corner looks ugly but there are many other types of markers you can use.
Once you have the coordinates of the ROI’s you can easily extract them and possibly OCR the data you need.
If you want to OMR a page where you have no control over the template you need to do some heuristics to find some sort of markers on the page (for example looking for a logo or line detection).
You can easily add an extension for multiple choice or checkboxes and extract the ROI to determine the selection.
In real applications you will want to create your own template dynamically and encode the ROI data somewhere so you do not have to manually enter the coordinates of the marker and ROI’s.

Source Code

Source on Github: https://github.com/ayoungprogrammer/OMR-Example


#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <zbar.h>
#include <iostream>

using namespace cv;
using namespace std;
using namespace zbar;

//g++ main.cpp /usr/local/include/ /usr/local/lib/ -lopencv_highgui.2.4.8 -lopencv_core.2.4.8

void drawQRCodes(Mat img,Image& image){
  // extract results 
  for(Image::SymbolIterator symbol=image.symbol_begin(); symbol != image.symbol_end();++symbol) { 
    vector<Point> vp; 

    //draw QR Codes
    int n = symbol->get_location_size(); 
    for(int i=0;i<n;i++){ 
    RotatedRect r = minAreaRect(vp); 
    Point2f pts[4]; 
    //Display QR code
    for(int i=0;i<4;i++){ 

Rect makeRect(float x,float y,float x2,float y2){
  return Rect(Point2f(x,y),Point2f(x2,y2));

Point2f rotPoint(Point2f p,Point2f o,double rad){
  Point2f p1 = Point2f(p.x-o.x,p.y-o.y);

  return Point2f(p1.x * cos(rad)-p1.y*sin(rad)+o.x,p1.x*sin(rad)+p1.y*cos(rad)+o.y);

void drawRects(Mat& img,Point2f rtr,Point2f rbl){
  vector<Rect> rects;

  Point2f tr(1084,76);
  Point2f bl(77,1436);



  //Fix rotation angle
  double angle = atan2(tr.y-bl.y,tr.x-bl.x);
  double realAngle = atan2(rtr.y-rbl.y,rtr.x-rbl.x);

  double angleShift = -(angle-realAngle);

  //Rotate image
  Point2f rc((rtr.x+rbl.x)/2,(rbl.y+rtr.y)/2);
  Mat rotMat = getRotationMatrix2D(rc,angleShift/3.14159265359*180.0,1.0);

  rtr = rotPoint(rtr,rc,-angleShift);
  rbl = rotPoint(rbl,rc,-angleShift);

  //Calculate ratio between template and real image
  double realWidth = rtr.x-rbl.x;
  double realHeight = rbl.y-rtr.y;

  double width = tr.x-bl.x;
  double height = bl.y - tr.y;

  double wr = realWidth/width;
  double hr = realHeight/height;


  for(int i=0;i<rects.size();i++){
    Rect r = rects[i];
    double x1 = (r.x-tr.x)*wr+rtr.x;
    double y1 = (r.y-tr.y)*hr+rtr.y;
    double x2 = (r.x+r.width-tr.x)*wr +rtr.x;
    double y2 = (r.y+r.height-tr.y)*hr + rtr.y;

int main(int argc, char* argv[])
  Mat img = imread(argv[1]);

  ImageScanner scanner; 
  scanner.set_config(ZBAR_NONE, ZBAR_CFG_ENABLE, 1); 

  namedWindow("OMR",CV_WINDOW_AUTOSIZE); //create a window

  Mat grey;

  int width = img.cols; 
  int height = img.rows; 
  uchar *raw = (uchar *)grey.data; 
  // wrap image data 
  Image image(width, height, "Y800", raw, width * height); 
  // scan the image for barcodes 

  //Top right point
  Point2f tr(0,0);
  Point2f bl(0,0);

  // extract results 
  for(Image::SymbolIterator symbol = image.symbol_begin(); symbol != image.symbol_end();++symbol) { 
    vector<Point> vp; 

   //Find TR point
     tr = Point(symbol->get_location_x(3),symbol->get_location_y(3));

   //Find BL point
     bl = Point(symbol->get_location_x(1),symbol->get_location_y(1));

  imwrite("omr.jpg", img); 

  return 0;

Tutorial: Scanning Barcodes / QR Codes with OpenCV using ZBar

Categories Computer Vision, Uncategorized
With the ZBar library, scanning Barcodes / QR codes is quite simple. ZBar is able to identify multiple bar code /qr code types and able to give the coords of their locations.

This tutorial was written using:
Microsoft Visual Studio 2008 Express
OpenCV 2.4.2
Windows Vista 32-bit
ZBar 0.1

You will need OpenCV installed before doing this tutorial

Tutorial here: http://ayoungprogrammer.blogspot.ca/2012/10/tutorial-install-opencv-242-for-windows.html

1. Install ZBar (Windows Installer)


Check install developmental libraries and headers (You will need this)

Install ZBar in the default directory
“C:Program FilesZBar”

2. Import headers and libraries

Tools ->Options 

Projects & Solutions -> VC++ Directories
Go to “Include files” and add: “C:Program FilesZBarinclude”

 Go to “Library files and add: “C:Program FilesZBarlib”

3. Link libraries in current project

Create an empy blank console project
Right click your project -> Properties -> Configuration Properties -> Linker -> Input

In additional dependencies copy and paste the following:

4. Test Program

Make a new file in your project: main.cpp
 #include "zbar.h"  
 #include "cv.h"  
 #include "highgui.h"  
 #include <iostream>  
 using namespace std;  
 using namespace zbar;  
 using namespace cv;  
 int main(void){  
      ImageScanner scanner;  
      scanner.set_config(ZBAR_NONE, ZBAR_CFG_ENABLE, 1);  
       // obtain image data  
      char file[256];  
      Mat img = imread(file,0);  
      Mat imgout;  
      int width = img.cols;  
      int height = img.rows;  
   uchar *raw = (uchar *)img.data;  
   // wrap image data  
   Image image(width, height, "Y800", raw, width * height);  
   // scan the image for barcodes  
   int n = scanner.scan(image);  
   // extract results  
   for(Image::SymbolIterator symbol = image.symbol_begin();  
     symbol != image.symbol_end();  
     ++symbol) {  
                vector<Point> vp;  
     // do something useful with results  
     cout << "decoded " << symbol->get_type_name()  
        << " symbol "" << symbol->get_data() << '"' <<" "<< endl;  
           int n = symbol->get_location_size();  
           for(int i=0;i<n;i++){  
           RotatedRect r = minAreaRect(vp);  
           Point2f pts[4];  
           for(int i=0;i<4;i++){  
           cout<<"Angle: "<<r.angle<<endl;  
   // clean up  
   image.set_data(NULL, 0);  

5. Copy libzbar-0.dll from C:/Program Files/ZBar/bin to your project folder

6. Run program 

Sample Images