An Image Processing Oriented Optical Mark Reader Based on ... · by using this technology the conduction of exam is getting much easier, powerful, and cheap [11]. 2SWLFDO 0DUN 5HDGHU

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An Image Processing Oriented Optical Mark Reader Based on Modify

Multi-Connect Architecture MMCA

Rusul Hussein Hasan1,Emad I Abdul Kareem

2

1, 2 College of education in Computer since, AL-MustansiriyaUniversity, Iraq. Baghdad.

Abstract— Optical Mark Recognition (OMR) is the technology of electronically extracting intended

data from marked fields, such as squareand bubbles fields, on printed forms. OMR technology is

particularly useful for applications in which large numbers of hand-filled forms need to be processed

quickly and with a great degree of accuracy. The technique is particularly popular with schools and

universities for the reading in of multiple choice exam papers. This paper proposed OMRbased on

Modify Multi-Connect Architecture (MMCA) associative memory, its work in two phases: training

phase and recognition phase.The proposed method was also able to detect more than one or no

selected choice. Among 800 test samples with 8 types of grid answer sheets and total 58000

questions, the system exhibits an accuracy is 99.96% in the recognition of marked, thus making it

suitable for real world applications.

Key Words: Associative memory, Image processing, Optical mark reader and multiple choice exam.

I. INTRODUCTION

OMR technology has changed much in recent years. Now a day in schools, colleges and classes

OMR technology is used. Exams are conducted using OMR answer sheet checking system because

by using this technology the conduction of exam is getting much easier, powerful, and cheap [11].

Optical Mark Reader (OMR), also called “mark sensing”,is a method of entering data such as

assessment/multiple-choice exams, course evaluation sheets, enrollment forms, surveys etc. into a

computer system using an optical mark reader. Pencil or pen marks made in predefined positions on

paper forms as responses to questions or tick list prompts can be read by the reader. These marks are

digitally entered into a computer for further analysis. OMR is very useful when data is to be

collected from a large number of sources simultaneously and a large volume of data must be

collected and processed in a short period of time. The university environment is the perfect

application area for OMR systems. Especially when dealing with large class sizes. The government

and the medical industry are also major application areas for OMR systems [15].

The idea of such a system was proposed by R. B. Johnson, a high school science teacher in

Michigan, who devised a machine for recording students test answers and to compare them to an

answer key. IBM bought rights to his invention and launched the machine in the market by the name-

IBM 805 Test Scoring Machine [1].

The proposed approach will use modify multi-connect architecture associative memory which is

developed from MCA associative memory [3]it is learning on mask initialled to detection mark

fromeach question during recognition phase.

II. RELATED WORKS

This subsection provide a survey of the literature related to Optical Mark Reader. That were

developed to improve Optical Mark Reader.

2.1 Optical Mark Reader Based Image Processing

According to the literature survey to the related works, two techniques have been used as follow:

2.1.1 OMR Based Image Segmentation and Thresholding Technique

Chinnasarn et al, presented a system which was based on Personal Computer-type microcontroller

and image scanner. The system operations can be distinguished in two modes: learning mode and

International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 02, Issue 07, [July– 2015] ISSN (Online):2349–9745 ; ISSN (Print):2393-8161


operation mode. The data extraction from each area can be performed based on the horizontal and

vertical projections of the histogram. For the answer checking purpose, the number of black pixels in

each answer block is counted, and the difference of those numbers between the input and its

corresponding model is used as a decision criterion. This is the first image-based OMR technique

[4].

Andrea Spadaccinidescribed JECT-OMR, a system that analyses digital images representing scans of

multiple-choice tests compiled by students. The system performs a structural analysis of the

document in order to get the chosen answer for each question, and it also contains a bar-code

decoder, used for the identification for additional information encoded in the document. JECT-OMR

was implemented using the Python programming language, and leverages the power of the Gamera

framework in order to accomplish its task. [6].

Tien Dzung Nguyen et al. proposes grading multiple choice test which is based on a camera with

reliability and efficiency. The bounds of the answer sheet image captured by the camera are first

allocated using Hough transform and then skew-corrected into the proper orientation, followed by

the normalization to a given size. The tick mark corresponding to the answer for each question can

be recognized by allocation of the mask which wraps the answer area [7].

NutchanatSattayakawee proposes the algorithm of test scoring for grid answer sheets. The method

used is based on projection profile and thresholding techniques [8].

Rakesh S et al. proposed system consists of an ordinary printer, scanner and a computer to perform

computation and is assisted with a graphical user interface. Users can design forms of their choice

and use it for survey or other related activities. The filled forms are scanned and scanned images are

given as input to a computer, which does the computation and stores the result in a user

understandable spreadsheet. The system is independent of hardware and system platform, thereby

making it platform independent [9].

A. AL-Marakebypresents a low cost and fast solution for optical mark recognition system working in

multi-core processor system. The answer sheet is captured using a digital camera and the image is

processed. Initially the borders of the sheet are located then the bubbles are detected. Fast techniques

are used to detect the bubbles without a rotation correction. An adaptive binarization has been used

to overcome the lighting effects of the camera based images [10].

2.1.2 OMR Based Image Segmentation and Template MatchingTechnique

Francisco de AssisZampirolli et al. presents a simple and innovative method to transform captured

images of answer sheets into reduced binary matrices containing answers to the questions plus some

control elements, using simple morphological operations for segmentation [5].

AzmanTalib et al. proposes shape-based vision algorithm, a hierarchical template-matching approach

that implemented in this system to verify the imaging and inspecting the correct answer of the

Optical Mark Recognition (OMR) sheet form. An OMR answer sheet scheme with all correct

answers are marked on the paper and will be used as a template for object recognition during the

matching process. Region of interest (ROI) is selected and filtered into grey level to extract the

contour of the object. The image is then pre-processed and trained using image processing technique.

A low-cost 1.3 MP web camera is used to acquire the marked OMR, image for all questions together

with the sequence number; this is to ensure the system can distinguish between different

questions having the same answer [12].

Ms.Sumitra B. Gaikwadaims to develop Image processing based Optical Mark Recognition sheet

scanning system. Find that lot of competitive exams are being conducted as entrance exams. These

exams consist of MCQs. The students have to fill the right box or circle in the appropriate answer to

the respective questions. During the inspection or examining phase normally a stencil is provided to

the examiner to determine the right answer to the questions. This is a manual process and a lot of

errors can occur in the manual process such as counting mistake and many more. To avoid this

mistakes OMR system is used. In this system OMR answer sheet will be scanned and the scanned



image of the answer sheet will be given as input to the software system. Using Image processing

[11].

III. MODIFY MULTI-CONNECT ARCHITECTURE ASSOCIATIVE MEMORY

Associative memoryis a data collectively stored in the form of a memory or weight matrix, which is

used to generate output that corresponds to a given input, can be either auto-associative or hetero-

associative memory [2]. A Hopfield neural network is one of the most commonly used neural

network models for auto-association and optimization tasks, it has several limitations. For example,

it is well known that Hopfield neural networks has limited stored patterns, local minimum problems,

limited noise ratio, retrieve the reverse value of pattern, and shifting and scaling problems [3].

Although, MCA has been overcome these limitations [3], to improve the efficiency of MCA in order

to decrease the network size and weight size. In additional to increase the ability for noise robust as

well as speed up its learning and convergence process. A modified associative memory based on the

MCA, namely the Modify Multi Connect Architecture (MMCA), is proposed. The modifications

include the network architecture as well as in its learning and convergence processes.

This improving was done by proposed algorithms for learning process and convergence process.

Thus for both, the pattern (pattern: It means a sequence of 1's and -1's) will divide into a number of

parts with size two, to be considered as a vector v (eachtwo element of the pattern will be one

vector). Each one of these vectors needs to create its learning weight matrix during learning process

or need to find the convergence pattern during the convergence process see Figure 1.

Figure 1. The data (pattern) divided into a number of vectors with size two, which it need

to create its learning weight matrix.

Because of this process, MMCA can deal with any pattern size and the associative memory capacity

became unlimited, and it could remember even the correlation patterns. Additionally, because the

size of the vectors is two, there are no more than four possible vectors see Table 1, this means there

are no more than two weight matrices W will be built during learning process depending on the fact

that each pair of orthogonal vectors has the same weight. These matrices are symmetric, without zero

diagonal and with size 2*2.

Table 1. Illustrated the four possibilities of the bipolar vector with length two.

The architecture of MMCA is illustrated in Figure 2. It shows each path represents one learning

weight matrix (1< m <2), thus, all the vectors in the pattern will be replacing with a number,

which represents the number of the path in the net, by this number, we can call the path again.

Figure 2. The architecture of MMCA associative memory.

-1 -1

-1 1

1 -1

1 1



IV. THE PROPOSED OMR MODULE

The general flowchart of OMR module as shown in Figure 3.Where the answer sheet captured by a

scanner is then processed by the system and the assessment results arestoring in the excel file. The

details of each phase is now discussed.

4.1 Image Acquisition

The role of scanner is just to scan the filled sheet and so any flatbed or ADF (Automatic Document

Feeder) scanner is used to scan the sheet, in our study, high speed scanner is used to acquire the

images of attendance sheets. The average processing speed is more than 60 pages per minute. Image

data are transferred from scanner to computer and stored in memory of the computer with JPG image

format.

Figure 3: Flowchart of OMR

4.2 Image Pre-Processing

The pre-processing phase consists in a set of operations that make the scanned image more suitable

for the further phases.The first operation performed to the image is the conversion to gray scale; then

the image is converted into black and white format using the thresholding method.

Next the system does a compensation of rotation effects induced by the scanning operation. The

goal of this step is rotate of image answer sheet at a calculated angle to restore it to its normal

rectangle. To do that, at first we must calculate the correct angle by using Hough transform method,

and then apply bilinear interpolation method with correct angle to rotate all image answer sheet

pixels to normal location. Figure 4 shows image answer sheet before and after rotation operation.



(a) (b)

Figure 4: The rotation of image answer sheet: (a) rotate image answer sheet (b) Normal image answer sheet.

4.3 Answer Area Allocation

In this step image answer sheet is projected horizontally and vertically to located answer area.To

achieve Answer Area Allocation, the following steps must be applied sequentially:

Invert image answer sheet to binary image.

Compute vertical projection of an invert image answer sheet by counting of white values in each column. In Figure 5 (a), from mid of image answer sheet vertically, red line represents

direction of computing process and the blue arrow represents column, which contains the

maximum of white values. Finally, store the result from this process in look up table, size of

this array is width of image answer sheet divided by two, index of array represents the

number of column, and the data of array represents the count of white values in each column.

Compute horizontal projection of an invert image answer sheet by counting of white values in

each row. In Figure 5 (a), from mid of image answer sheet horizontally, violet line represents

direction of computing process and the green represents the row which contains maximum of

white values. Finally, store the result from this process in look up table, size of this array is

height of image answer sheet division by two, index of array represents the number of row

and the data of array represents the number of white values in each row. Figure 5 (b) shows

answer area allocation after cropping horizontal and vertical projection.



(a) (b)

Figure5: The cropping of vertical and horizontal projection.

(a) Original image answer sheet (b) after cropping of vertical and horizontal projection.

Then each question zone must be determined in order to score the response of that question.

Question number is excluded from question segment. Each question comprises 4 positions (X up, X

low, Y up, Y low) which are lower and upper bounds of the question answer zone as shown in Figure 6.

Figure 6: Question zone represented by the quadruple (X up, X low, Y up, Y low).

4.4Answer recognition using MMCA

First, initial mask size it computation in the Equation 2.The proposed OMR approach will use this

mask as a training image during learnphaseis implemented only once and save it in a lookup table in

MMCA associative memory to be remembered during recognition process.

Mask size = (X up-X low)/N choice (2)

Where recognition phase implements for each question the convergence phase of the MMCA using

the lookup table that is built during the training Phase.Figure7illustrates the training and recognition



phase steps to implement of MMCA method

Figure7:illustrates the tanning and recognition phase steps to implement of MMCA method.

V. RESULTS DISCUSSION AND ANALYSIS

In the experiments, percentage of correctness was measured. Two types of answer sheets

have been used, each type divide in four classes as shown in Appendix A.Each class was defined by

100 samples. The accuracy result and process time for each test are shown in Figure 8 and Figure 9

respectively.

Figure 8: accuracy result of each test.

99.5

99.6

99.7

99.8

99.9

100

100.1

506080100506080100

ISquare

IISquare

IIISquare

IVSquare

IBubble

IIBubble

IIIBubble

IVBubble

Acc

urc

y

Accuracy result of MMCA



Figure 9: Process time of each test.

The process time in these experiments depend the form type as shown in the Figure 8 the process

time is increase when the number of question increase. The performance evaluations of the proposed

OMR have been shown in Appendix B.Total number of questions were 58000. They aretested in two

types of answer sheets. The experiments show that there are just 26 answers were unrecognized

fromthe total number of questions (i.e. the 58000 questions). Accordingly,the average accuracy

was99.96%.

VI. COMPARISON WITH OTHER WORKS

The review presented in the related work shows that there are nine papers that have a similar goal

with the present research. These papers used the threshold and template matching technique to

detected mark. Therefore, it is useful to compare the proposed OMR module in this research with the

method in those papers. Hence, a comparison study has been conducted by evaluating these paper

work.

In this comparison, focused on compare with the accuracy result only for all question in each paper.

Figure 10 shows average accuracy for all paper that compare them.

Figure 10: Average accuracy comparison.

0

20

40

60

80

100

120

140

160

506080100506080100

I SquareII SquareIIISquare

IVSquare

I BubbleII BubbleIIIBubble

IVBubble

Seco

nd

s

Average Speed Of MMCA

90.00%91.00%92.00%93.00%94.00%95.00%96.00%97.00%98.00%99.00%

100.00%101.00%



VII. CONCLUSION Results Discussion and Analysis show that the proposed OMR average accuracy was 99.96. This

work focused on the development of an OMR for multiple-choice tests via using a new technique

(i.e. associative memory with modify multi-connect architecture) paving the way to the future works

to develop more efficient OMR in speed and accuracy using associative memory (may be after

modified it). The input forms used in the experiments were printed on an A4 sheets. No need to use

OMR scanner, where a normal scanner is used to scan the filled forms. The scanned copies are then

used as input to the proposed OMR.The actual error was caused by some pale marks form input. And

the proposed method also able to detect more than one or no selected choice.

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Appendix A

Type Of answer Sheet Class No. Of Question

Square I 50

II 60

III 80

IV 100

Bubble I 50

II 60

III 80

IV 100



Appendix B

Type Of answer Sheet Class No. Of Question Total No. Of Answer Recognized Unrecognized

Square I 50 5000 5000 0

II 60 6000 6000 0

III 80 8000 8000 0

IV 100 10000 9998 2

Bubble I 50 5000 5000 0

II 60 6000 5984 16

III 80 8000 7997 3

IV 100 10000 9995 5

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An Image Processing Oriented Optical Mark Reader Based on ... · by using this technology the conduction of exam is getting much easier, powerful, and cheap [11]. 2SWLFDO 0DUN 5HDGHU