Exponential Decay Weighted k-NN for Sensor Data Imputation: An Application in Assamese Manuscript Imaging

Exponential Decay Weighted k-NN for Sensor Data Imputation: An Application in Assamese Manuscript Imaging | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 January 2026 V1 Latest version Share on Exponential Decay Weighted k-NN for Sensor Data Imputation: An Application in Assamese Manuscript Imaging Authors : Aryan Kumar Singh 0009-0006-0694-6659 and Pranita Baro [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176924171.10314842/v1 113 views 45 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Manuscripts, which have significant cultural and linguistic values, suffer from degradation associated with aging, exposure to the environment, and noise introduced by sensors during digitization. As a result, reconstructing the missing pixels in such images is an important area for considered digital preservation and analysis. This study proposed an Exponential Decay Weighted k-Nearest Neighbor (EDW-kNN) algorithm to improve the accuracy of imputations by utilizing the effect of closeness with spatial neighbors and limiting the impact of noisy distant pixels. We examined EDW-kNN compared to a number of baseline and sophisticated imputation methods, including Mean, Median, Mode, kNN, Weighted kNN (WkNN), Multiple Imputation by Chained Equations (MICE), and a Convolutional Neural Network (CNN)-based method. We tested these methods using a dataset of Assamese manuscripts which include up to 70% missing data. Our results indicated EDW-kNN produced the lowest Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for all implementations, with higher missingness pointing towards stronger performance in comparison to the alternative methods. We also conducted a sensitivity analysis of the decay parameter α to understand the robustness of EDW-kNN across a diverse level of missing data. Overall, results support the effectiveness and computational efficiency of EDW-kNN, suggesting that this model would be transferable to other application domains including heritage document restoration and similar image imputation studies. Supplementary Material File (concurrency_and_computation__practice_and_experience.pdf) Download 762.49 KB Information & Authors Information Version history V1 Version 1 24 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cultural heritage restoration exponential decay weighting nearest neighbor algorithm pixel imputation Authors Affiliations Aryan Kumar Singh 0009-0006-0694-6659 Indian Institute of Science Department of Computational and Data Sciences View all articles by this author Pranita Baro [email protected] CSIR Fourth Paradigm Institute View all articles by this author Metrics & Citations Metrics Article Usage 113 views 45 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aryan Kumar Singh, Pranita Baro. Exponential Decay Weighted k-NN for Sensor Data Imputation: An Application in Assamese Manuscript Imaging. Authorea . 24 January 2026. DOI: https://doi.org/10.22541/au.176924171.10314842/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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