Vol 1, Issue 3 | July 2026

Most of our valuable aged records have distortion. It might be because of poor maintenance, poor paper quality, prolonged disturbances like blurring of ink and damages done by bookworms like silverfish and booklouse. Although paper documents are omnipresent in our society, there is a need to have many of these documents stored in electronic format to enhance their lifespan and readability. The conversion might be adopted by scanning, printing, photocopying and faxing also introduce distortions to the images. Human beings often notice the distortions present in photocopies taken from photocopies of text documents. Smaller distortions, which might not be noticeable by humans, are still large enough to interfere with the ability of a computer to read text. It was these practical complications which formed the base for this paper. In this paper an attempt is made by Simulink towards distorted document image using Sobel edge detection algorithm. The main goal of edge detection is to locate and identify sharp discontinuities from an image that helps to find the edges in an input image by approximating the gradient magnitude of the image.

Learning an optimal Bayesian belief network for a Bayesian network classifier is a NP-hard problem. A number of heuristic-based algorithms have been proposed for supervised learning of Bayesian belief network such as Tree-Augmented Naive Bayes (TAN). In the past decade, bio-inspired swarm intelligence (SI) based algorithms have been proposed for many optimization problems. Swarm Intelligence based algorithms are characterized by the collective decentralized decision-making of several independent agents to search for the optimal solution in the solution search space. In this paper, we propose a novel swarm intelligence based Hunting Group Search Bayesian Network (HuGS) algorithm to learn the Bayesian network for a Bayesian network classifier. HuGS has been inspired by the behavior of a pack of hunting animals such as wolves. The classification accuracy of the proposed HuGS algorithm is tested against other Bayesian network classifiers such as Naive Bayes and TAN. The performance of HuGS is evaluated using 20 benchmark datasets from the UCI classification datasets. Our experiments show that HuGS outperforms the other state of the art Bayesian network learning algorithms.