2011 International Conference on Advanced Technologies for Communications (ATC 2011)
Efficient and reliable camera based multiple-choice test grading system Tien Dzung Nguyen, Quyet Hoang Manh, Phuong Bui Minh, Long Nguyen Thanh, Thang Manh Hoang School of Electronics and Telecommunications, Hanoi University of Science and Technology, Vietnam Email:
[email protected]
Abstract - This paper proposes a new idea for grading
to
multiple-choice test which is based on a camera with
corresponding OMR software are the highlights of them.
resolve
this
problem.
An
OMR
machine
and
the
reliability and efficiency. The bounds of the answer sheet image captured by the camera is first allocated using
The scanning machine, referred to as OMR scanner, can
Hough transform and then skew-corrected into the proper
hold a large numbers of forms and read them as they are
orientation, followed by the normalization to a given size.
automatically fed through the machine. The machine-based
Next, the tick mark corresponding to the answer for each
process is designed to score a mass of students' answer sheets
question can be recognized by allocation of the mask which
which is special kind of paper, known as 'transoptic' paper. It
wraps the answer area. The experimental results showed
uses sensors to score students' responses by determining
that
whether the pre-defmed position is blank or already marked.
the
proposed
system
has
achieved
significant accuracy,
This mechanism greatly reduces the system's consuming time.
reliability, and elapsed time compared with those of the
It is clearly seen that the season why OMR scanner operates
improvement
in
performance
in
terms
of
conventional optical mark recognition (OMR) systems.
accurately at a high speed. OMR scanner provides the best
The proposed system also demonstrated that it can also
solution for the Assessment Services but exceeds any needs of
achieve high accuracy of 99.7% while using non-transoptic
small and medium-sized educational institutes or schools. The
answer sheet paper with lower cost.
main reasons why most of schools are not in favor of using OMR machine are its price and operating cost due to MCQ scoring papers which are more expensive than plain papers.
Key words: Multiple-choice test; grading system; camera based, tick mark.
While OMR scanner's price makes this machine suitable I.
for only some dedicated purposes, the OMR software seems to
INTRODUCTION
be a better choice for small sized schools. However, there are
Although the use of online computer-assisted assessment (CAA) can significantly reduce the burden
associated with
testing
is
a
large
numbers
of
students,
it
hard
to
still some limitations on implementing OMR software because a scanner must be employed to convert answer sheets into
be
images.
implemented because the system is too expensive to set up
sheets but it is also a costly one compared to Flatbed scanner
being widely used as an effective assessment or grading high
which
school and university students [2][3]. Nowadays, MCQs has
on Optical
by
hand.
Besides
meets the appropriate price and overcomes conventional
Mark
application.
Recognition technology (OMR) [5][6], have been developed
Proposed Algorithm Captured Images from camera
Images
Skew
Enhancment
Adjustment
Normalization
Answers' area allocation
Fig.l. Block diagram of the proposed method for camera based multiple-choice test grading system
978-1-4577-1207-4/11/$26.00 ©2011 IEEE
the
problems. Obviously, there is a lack of an application which
of MCQ is more demanded, a manual grading solution seems based
handled
Offset [7]. It is impossible for scanners to avoid such these
coverage within a limited time period. However when the use applications
slowly
confronted with several scanning problems such as Skew and
can assess students with the broad range of knowledge
Several
be
when dealing with ideal scanned images but they are being
examination over the world, particularly in Vietnam because it
[4].
must
availability of many OMR software, there are no problems
become a fast and reliable method for national entrance
harder
An Automatic Document Feeder (ADF) image
scanner can provide a quite high speed of scanning answer
and maintain [1]. Multiple-choice questions (MCQs) are still
268
Database
The objective of this paper is to discuss how to simplify the
Typically this problem can be solved from edge
multiple-choice test grading system by adopting the use of a
detection process applied for the border lines [13].
camera
However in this work, we utilize the Hough transform
instead
of
a
scanner.
A
camera
has
significant
[14] to determine the skew angle of the border line,
advantages over a scanner such as capturing speed and setting.
where the border line is mapped to rho-theta space.
It is apparent that a camera captures images as fast as an ADF image scanner does.
From Fig. 2. we can see that the most highlighted
The speed of image digitizing step
location in this space would correspond to the input
reduces the total processing time of the system. The proposed
border line and its theta angle would determine the
grading system also uses a automatic paper feeder which provides more options of number of answer sheets.
skew angle of the border line.
The
scanning problems above which may introduce into capturing image can be managed by a suitable algorithm. The skew is no longer a problem when the proposed algorithm uses skew detection to correct it [8][9][10]. The offset is also diminished by precise paper feeding mechanism. In addition, this system works well with plain papers instead of using pricey transoptic papers. The proposed system showed to be more compact than scanner-based
or
machine-based
ones
with
efficient
and
reliable performance. In the next section, the block diagram of the proposed MCQs
Fig. 2. Skew detection using Hough transform
system is introduced where the techniques used for each stage is described in details. Section III deals with results and The
performance evaluation on real answersheet database.
Skew adjustment
•
conclusion and future research is fmally discussed in section
This
IV.
step
will
correct
border
line
and
then
all
components in the captured answersheet to be aligned II.
in horizontal axis based on the detected skew angle.
PROPOSED SYSTEM
Fig. 3 shows an example of a skew corrected image
The block diagram of the MCQs system is illustrated in Fig.
after applying the proposed method.
1, where the answersheet captured from camera is then processed by the system and the assessment results are imported to the storing database for students to be assessed. The details of eachstage is now discussed. .. "
A. Image Enhancement
. .
:
u •
•
-
In this step, the input answersheet captured the camera is enhanced by histogram equalization followed by median and average filtering [11].
After that the resulting image is
binarized using Otsu's method [12] to determine the answer tick marks and markers located on the answersheet border. Fig. 3. Skew corrected image
B. Skew Correction
Skew problem may cause
to
inaccurate
results
from
C. Normalization
identifying students' marks. This module actually addresses to
This module deals with a scaling issue to normalize a
skew detection and correction of the input answersheet into
captured answersheet image into a given size. This is because
the normal orientation. The proposed algorithm is applied on
of the location of a camera or an image size in use. With the
the enhanced image to detect and adjust the skew angle due to
normalization, the allocation of regions of interest inside the
improper capture position of the camera. The two steps in this
image becomes easier since the size of the designed regions
algorithm are briefly described: •
are
Skew detection
known
when
the
answersheet.
the
region
of
interest
bounded
markers
should
be
cropped
normalization, detaected
From the fact that all components such as letters, tick
we
design
from
Before
the
the
lines
original
captured answersheet.
mark and lines in the answersheet are oriented in the same direction, the skew detection after capturing can
Fig. 4 shows the concept of normalization of an image into the
be performed.
given size, which is determined by the scaling ratio as:
The skew angle of the captured answersheet image would be based on the determination of the border line orientation compared to the perpendicular vertical axis.
269
where R stands for the scaling ratio, W the width of the input
•
image, and WN the width of the normalized image.
Recognition of the selected answer tick marks Thanks region
to the normalization can
be
then
process,
divided
into
each image
4
sub-regions
containing the answer choices. For each of the 4 sub regions, the number of black pixels is accumulated and then the choice corresponds to the sub-regions with the maximum number of black pixels will be assigned as the selected answer by the student. As the matter fact, there may be
the case that
none of sub-regions
contains a tick mark, when the number of black pixels is zero or less than a given threshold. In addition, there may exist two or more sub-regions where the number of black pixels is larger than a given threshold. In these cases, the answers for the given questions are considered as invalid and the selected answers are assigned as incorrect ones, The process is repeated for the other masks which wrap the remaining answer area. The results are then stored in the designed Fig.
database which contain information about the student
4. Nonnalization concept
list to be assessed. Fig. 6 demonstrates a part of the answersheet where the tick masks of the student were correctly recognized by our method.
D. Allocation of the answer area. r--F 51 52 '.. 53 • 54 A. 55 'b 56
57 58
59 60
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