Automated Digitization of Student’s Marks from the Answer-Book Images Using a Lightweight CNN Model

Preparing student’s digital marksheet using images of student answer-books is a potential application in academic institutions. Segmenting assigned marks automatically from answer-book images is extremely challenging, and it also demands pre-processing before the recognition stage. In addition, recognizing handwritten digits is crucial due to different writing styles. Existing research admits the superior performance of deep learning-based models in handwritten digit recognition (HDR) applications for popular datasets. However, their implication on real-time data for an experimental setup needs much attention. This paper presents an experimental setup that uses student answer-book images to record students’ marks digitally. We proposed a lightweight convolutional neural network (CNN) model for HDR. We also introduced a contour-based segmentation process for automatically extracting student details from answer-book images. The obtained results show the state-of-the-art performance of our proposed CNN model for real-time images. Further, introducing additional pre-processing before recognition significantly enhances the accuracy of the HDR experimental setup.