Online Signature Verification (OSV) systems are crucial for secure authentication in digital environments, where handwritten signatures are electronically captured and verified. These systems use machine learning and deep learning methods to analyze unique dynamic features of a signature, such as pen pressure, speed, and stroke order, in addition to its static shape. Recent approaches leverage techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and hybrid models, to analyze the unique dynamic and static features of signatures. However, there exists a problem of designing real-time systems that are responsive, and have reliable performance using the mentioned deep learning techniques because these models have to be trained each time a new user is added to the database. The objective of our experiment is to build an OSV system that integrates a CNN-based Siamese Network and a webpage created using ReactJS to allow the user to upload their signature or store it in the database. The model extracts spatial features from the signatures and makes decisions based on the similarity of the uploaded signature with the original signature of the user. The system is designed to handle real and forged signatures through continuous model training. Using a combination of signature preprocessing techniques, feature extraction, and classification models, this system aims to ensure a robust, reliable, and secure method for identity verification in online transactions and sensitive digital applications,

Feature descriptors play a crucial role in image retrieval by representing images in a compact and discriminative manner. This survey paper explores various techniques for preparing feature descriptors used in image retrieval systems. We categorize the approaches into traditional handcrafted descriptors and deep learning-based descriptors. For each category, we discuss popular techniques, their underlying principles, and their advantages and limitations. We also provide insights into recent advancements and benchmark datasets used for evaluation. In order to help academics and practitioners choose the best strategies for their applications, this survey attempts to give them a thorough grasp of feature descriptor preparation methods in image retrieval.

Document Clustering is the process of collecting similar kind of documents into one group based on any particular similarity function. Document clustering is also referred as text clustering. Informative features like phrases and their weights are considered to be more important to perform efficient document clustering. This paper mainly deals on two key parts for achieving efficient document clustering. The first part is a phrase based document model named as the Document Adjacency List, it explains about the construction of a phrase based model of the document set. It produces efficient phrase matching which is useful to decide the similarity among the documents. The second part is the document clustering algorithm that is proposed to enhance the Document Adjacency List for clustering based on the similarity measure. The combination of the above two parts leads to better calculation of similarity among documents and similarity further helps to calculate document clustering.