Title: KBSS: an efficient approach of extracting text contents from lecture videos - computational intelligence techniques

Authors: Sreerama Murthy Velaga; Panigrahi Srikanth; D. Khader Basha

Addresses: Department of Computer Science and Engineering, N S Raju Institute of Technology (NSRIT), Pendurthi, Visakhapatnam-531173, Andhra Pradesh, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, K L (Deemed to Be University), Bowrampet, Hyderabad, 500043, India ' Department of Computer Science and Engineering, GMR Institute of Technology, Rajam 532127, India

Abstract: For the last few decades, there is a lot of research going on in the areas of image processing and text mining. They became an emerging research area because an image or a video with cloud is a major source of data, whereas text is a prominent and direct source of information in a video lecture. The challenges usually faced are converting the lecture video frames into binary conversion matrix, extracting image to text matrix, defining the threshold value and classification. Here, in this paper an efficient approach for extracting text contents from metadata lecture videos with cloud is proposed. We built a frame work KBSS in which the frames are converted into binary matrix, then extracted key factors with a text matrix, then clustered with proposed similarity measures in order to reduce the matrix and classification of the text matrix using neural networks, and finally checked the proposed similarity measure with the properties of each case-wise. The objective is to extract text from meta lecture videos with cloud and improving algorithm performance.

Keywords: meta lecture video; computational intelligence techniques; binary matrix; key factors; text and image mining.

DOI: 10.1504/IJCC.2024.136277

International Journal of Cloud Computing, 2024 Vol.13 No.1, pp.1 - 24

Accepted: 07 Jan 2021
Published online: 26 Jan 2024 *