  In spite of the high accuracy of the existing optical mark reading (OMR)
systems and devices, a few restrictions remain existent. In this work, we aim
to reduce the restrictions of multiple choice questions (MCQ) within tests. We
use an image registration technique to extract the answer boxes from answer
sheets. Unlike other systems that rely on simple image processing steps to
recognize the extracted answer boxes, we address the problem from another
perspective by training a machine learning classifier to recognize the class of
each answer box (i.e., confirmed, crossed out, or blank answer). This gives us
the ability to deal with a variety of shading and mark patterns, and
distinguish between chosen (i.e., confirmed) and canceled answers (i.e.,
crossed out). All existing machine learning techniques require a large number
of examples in order to train a model for classification, therefore we present
a dataset including six real MCQ assessments with different answer sheet
templates. We evaluate two strategies of classification: a straight-forward
approach and a two-stage classifier approach. We test two handcrafted feature
methods and a convolutional neural network. In the end, we present an
easy-to-use graphical user interface of the proposed system. Compared with
existing OMR systems, the proposed system has the least constraints and
achieves a high accuracy. We believe that the presented work will further
direct the development of OMR systems towards reducing the restrictions of the
MCQ tests.
