Existing Optical Mark Recognition (OMR) systems tend to be expensive and rigid in their operation, often resulting in erroneous evaluations due to strict correction protocols. This scenario airs the need for a flexible OMR system. Hence, in this work, we propose a lightweight transfer learning based Convolutional Neural Network (CNN) model, dubbed as OMRNet, which can classify answer boxes on any generalized OMR test sheet. Unlike most existing techniques that rely on image processing algorithms to recognize extracted answer boxes in two classes: confirmed and empty, the OMRNet is designed to classify the answer boxes into confirmed, crossed-out, and empty categories. That is, OMRNet is facilitating the crossing out of previously answered questions and thus removing the rigidity of templates in Multiple Choice Question (MCQ) tests. We have built OMRNet on top of a MobileNetV2 backbone connected to four fully connected layers with appropriate dropouts and activation functions in between. We have evaluated OMRNet on the Multiple Choice Answer Boxes dataset available at
https://sites.google.com/view/mcq-dataset
. We have performed experiments following a 5 fold cross validation scheme, and OMRNet has achieved accuracies of 95.29%, 95.88%, 93.97%, 97.45%, and 97.20%, with an average accuracy of 95.96%. Also, the experimental results confirm that the present model performs better than the compared state-of-the-art methods and standard CNN models in...

OMRNet: A lightweight deep learning model for optical mark recognition

10.1007/s11042-023-15408-8