Assamese Script Identification and Content-Based Image Retrieval using Improved Siamese Neural Network - Sathiyaviradhan Janarthanan (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Content based image retrieval (CBIR) is a remarkable system that allows users to effortlessly search and retrieve images from a vast dataset. With a large number of images in the dataset, identifying similar images to our query image can be quite challenging. An effective and efficient approach is needed to utilize information from these image repositories. This system allows users to easily access Assamese Script images from the database by searching for specific content. Assamese is a language that falls under the Indo-Aryan language family and is one of the 22 scheduled languages in India. There is a wide range of Assamese printed documents available in digital format. However, retrieving information from these digital document images is a task of the highest priority. Current studies have shown remarkable precision rates in initial retrieval levels, such as the top 10 and top 20 images. However, these rates drop significantly in subsequent levels, like the top 40, 50, and 70. Therefore, the objective of this paper is to introduce a novel CBIR approach that attains high precision values across all retrieval levels. The system consists of three main stages: Preprocessing, feature extraction, and feature similarity matching. Effective feature extraction techniques are crucial for optimizing the performance of a CBIR system. Therefore, we suggest utilizing the Improved Siamese Neural Network (ISNN) as a technique for extracting shape-based features. The Cosine distance with ISNN is utilized to match the query image features provided by users and identify a particular word in the document. The experimental findings demonstrate that, in comparison to the other current approaches taken into consideration in this study, the suggested methods yield superior outcomes.