Abstract
Due to the increased level of digitalization, data exchange and collection has grown to a greater extent. This bulk data needs categorization else data collected will be meaningless. The exchange of multimedia data has also increased due to the availability of the Internet. Images collected need a proper categorization that can help in fetching the required data. Government documents need to be uniquely identified from the set of plenty of data to help in different kinds of proceedings. It could help to fetch images only of the required government document. Due to the increased size of data as well as an increased quantity of data, different document image identification techniques are time-consuming. The proposed model quickly identifies specific government documents from a chunk of images with ease. Various filtering categories are applied to ease the process of categorization. To speed up the process of categorization, selective efficient features are shortlisted which contributes majorly toward substantiation of a government document.
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Ghadekar, P., Kaneri, S., Undre, A., Jagtap, A. (2021). Digital Image Retrieval Based on Selective Conceptual Based Features for Important Documents. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_53
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DOI: https://doi.org/10.1007/978-981-15-5258-8_53
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