OCR and OMR Final Project Report

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OCR and OMR Final Project Report
OCR and OMR Final Project Report
Refi Sencia Dity / ICT-7
1. Introduction
We choose OCR and OMR as implementation to what we already learn according to
subject image processing during this semester. Through this final project, we
combine various image processing techniques and implemented it in a simple C/C++
code.
2. Technical Details
� Programming languages: C and C++
� Compiler: GCC
� Platform: Linux
� Software: ImageMagick
3. Design
The user interface (GUI) is build to make the user easier to use the FormReader. OCR
is used for reading letter (name) however; OMR is used for reading the answer part
(mark). These 2 functions (OCR and OMR) will combine together with the GUI to
perform their function properly. GUI itself is created using GTK+.
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4. Implementation
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OCR and OMR Final Project Report
Before OMR and OCR can work properly, there are 2 preliminary steps that should
be made:
1. Binarization
Binarizarion is used to simplify the image representation, with binarization,
image will only have 2 value, black (0) or white (255). We used the 200 as a
threshold for this project.
2. Inversion
OCR OMR
With inversion, the value 255 (white) will be change to 0 and vice versa. It
helps in the calculation.
GUI
4.1 Optical Mark Reader (OMR)
1. Binarization
2. Search Box Coordinate
Image Answer
3. Calculate Histogram
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OCR and OMR Final Project Report
Histogram is used to read the answer. There will be 5 boxes (20x20 pixels) for every
number. Later, histogram calculation is performed for each of boxes. The box that
has the maximum value of black (0) pixel will be chosen as the answer.
4.2 Optical Character Recognition (OCR)
Inverted Image
Name Box
Answer
1. Find coordinates of crosses
2. Extract subset of image located within crosses range.
3. Name box extraction
4. Frame/border elimination
5. Normalization
6. Database Matching
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1. Find coordinates of crosses
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OCR and OMR Final Project Report
To find the coordinates of crosses, we take a 50 x 50 pixels subset of image in
every corner and then we use histogram to find the center point of every
cross.
2. Extract subset of image located within crosses range.
After getting the coordinate of each cross, we use that coordinate to subtract
the box inside the cross.
3. Name box extraction
The coordinate of the name box can be calculates through the pre-defined
(left-top) coordinates. After getting the coordinate, we can extract the box
based on the coordinate that we get.
4. Frame Border Elimination
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OCR and OMR Final Project Report
To make name box free from noise, we need to eliminate the border that
may be exist. This elimination is done Using Connected Component
Extraction.
5. Normalization
Normalization done in2 steps:
1. Extract the letter from the box
For extract the letter, we assume that there is no noise in the paper,
this method done by calculate the histogram vertically and
horizontally, and searching for boundary. Once we get the boundary,
we can get the coordinate of each corner. After we get the coordinate
of each corner, extraction for the letter can be done
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2. Scaling
6. Matching
From the boundary, we can get:
Left Top Coordinate: (10,15)
Right Top Coordinate: (100,15)
Left Bottom Coordinate: (10,150)
Right Bottom Coordinate: (100,150)
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OCR and OMR Final Project Report
The box based on the size of the letter can cause the size of every
boxes is different and it will be difficult in the matching process.
Because of that reason, scaling process is needed before the matching
process can be done. Scaling is done using Nearest neighbor
interpolation.
Matching is done using K-Nearest Neighbors algorithm. It is done by
comparing each letter with database and calculates the distance of the pixel;
finally we choose the closest one as the answer.
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4.3 GUI
� Created using GTK+.
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OCR and OMR Final Project Report
o GTK+ is a highly usable, feature rich toolkit for creating graphical user
� How to use it:
interfaces which boasts cross platform compatibility and an easy to use
API. GTK+ it is written in C, but has bindings to many other popular
programming languages such as C++, Python and C# among others.
1. File – Open (Answer Sheet)
o Choose a PGM file (scanned form of an answer sheet).
2. File – Load (Answer File)
o Choose a txt file containing correct answers.
3. Click “scan” button
3. Right panel displays:
Format: [number],[answer]
o Score (calculated by comparing scanned answers with
correct answers)
o Name (read by OCR program)
o Scanned answers (read by OMR program)
o Correct answers (loaded from txt file)
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5. Evaluation
� Accepts PGM file only.
� Does not handle skewed images yet.
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OCR and OMR Final Project Report
� Tested on answer sheet filled with computer (not filled by human yet).
� OCR:
o Ignore noise.
o Letters connected to the frame cannot be read properly.
� No filter during file selection.
o User can feed any type of file although the program can only process
PGM files correctly.
� Does not use capital letters yet.
o Should use case-insensitive character comparison in C program.
� Graphical User Interface was designed mainly to demonstrate the program.
o It does not consider ease of use and other aesthetic aspects.
6. Additional Information
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Important files:
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OCR and OMR Final Project Report
� lib/imglib.cpp -> Libraries for basic image processing tasks (generic and reusable).
� lib/imgoplib.cpp -> Collection of image filters (e.g., scaling, connected comp
extraction, dilation).
� lib/formlib.cpp -> Libraries for form reader functionalities (specific for the
application).
� form-reader-cli.cpp -> Main file for CLI version of the application (only for testing).
� form-reader-gui.cpp -> Main file for GUI version of the application.
Important classes:
� GrayscaleImage -> This class represents a Grayscale Image (can be constructed
manually or read from PGM file).
� GrayscaleImageOp -> Abstract class for GrayscaleImage operation. Direct subclasses:
DilationOp, ConnectedCompOp, ScaleOp.
� Point -> This class can represent a coordinate (x, y).
� Dimension -> Self-explanatory. Consists of width and height.
� Rectangle -> This class can represent a region in the image (x, y, width, height).
� FormReader -> The core class for the form reader application. Contains form reader
functionality.
� OCR -> This class carries all the OCR tasks. This class is used within the FormReader
class.
7. References
� Gonzales, R. C. and Woods, R. E. Digital Image Processing, 3 rd Ed. Upper
Saddle River, NJ: Prentice Hall, 2010.
� “The GTK Project”. http://www.gtk.org/, accessed January 12, 2011.
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