Image Classification for Project-based Learning to Differentiate Diagram and Figures.

Open Access
Conference Proceedings
Authors: Asmara SafdarSara AliMuhammad SajidUmer AsgherYasar Ayaz

Abstract: This paper describes creation of a dataset and addresses an image processing problem in the field of education. A Convolutional Neural Network (CNN) based model is trained to classify the images extracted from academic documents. With the advent of distant learning mode and assessment criteria based on online submissions, there is a need to improve assessment approaches other than finding plagiarism. To enhance the understanding of the concepts, project-based learning (PjBL) in distant learning mode (DL) can be adopted. PjBL has proven successful even for complex engineering problems. It has been found out that PjBL of basic teaching assessment decreases the pressure on institutional resources while also making it easier and more practical for students. So, we are considering project reports or assignment as core source of evaluation. Extracting diagrams and software generated images (graphs and software generated object models) is focus for the current work as they reflect knowledge and main effort of a student especially in engineering academics. Here figures are referred as images of schematic representation to show the working or architecture of a work or a phenomenon. Software based images (sbi) include graphs, simulation images and software generated pictures or models. We aim to distinguish the diagrams and sbi from rest of the figures so it can be filtered out for further assessment. The data extracted is in the form of images. A CNN based classification model MobileNet is used to classify the images. The results show viability of the dataset and promising trend keeping in view the difficulty level of problem and size of dataset. Accuracy can be improved by adopting other approaches to train and clean data and by increasing the data set by extracting more images from same domain of problem.

Keywords: Image classification, project-based learning, machine learning

DOI: 10.54941/ahfe1001597

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