MADS: A Multi-modal Academic Document Segmentation Dataset for Smart Question Bank Management

Aich, U, Ghosh, S and Saha, T (2024) MADS: A Multi-modal Academic Document Segmentation Dataset for Smart Question Bank Management .

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Abstract

In today’s world, most major academic institutes and organizations conduct competitive exams to assess eligibility of students for admission or recruitment. Due to the rising craze among participants, traditional methods are not optimized enough to get ahead in the race. The inclusion of AI enabled tutoring is mandatory for such exams. One such area of implementation is smart question bank management system. Though we have large volumes of questions of competitive exams in physical mode, however, they are harder to process visually for systems as they consist of several types of text and non-text elements such as numbers, equations, images alongside textual paragraphs. For this purpose, we propose MADS, which is a multi-modal academic document segmentation dataset consisting of images of documents containing heterogeneous questions from the competitive exams like GMAT, GRE, GATE, SAT, UGC-NET. These documents consist of textual paragraphs along with numbers, images and equations. The dataset comes with bounding box annotation in two popular format YOLO and PASCAL-VOC formats to aid the development of efficient document segmentation algorithms. Additionally, benchmarks have been provided for state of the art deep learning based implementations such as Faster RCNN and YOLO-v8. From application point of view, the proposed dataset can identify different objects in an image so that later it can be used for semantic relationship and question answering applications enhancing comprehension and personalized learning experiences, thus, supporting the goal of providing quality education.

Item Type:	Conference Item (Unspecified)
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	02 Dec 2024 08:31
Last Modified:	07 Jun 2025 09:11
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3188968
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