{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:16:48Z","timestamp":1760235408995,"version":"build-2065373602"},"reference-count":9,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T00:00:00Z","timestamp":1630281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Document imaging\/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g., \u00a0libraries, office communication, managementof workflows, and electronic archiving [...]<\/jats:p>","DOI":"10.3390\/s21175849","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Document-Image Related Visual Sensors and Machine Learning Techniques"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0773-9476","authenticated-orcid":false,"given":"Kyandoghere","family":"Kyamakya","sequence":"first","affiliation":[{"name":"Institute for Smart Systems Technologies, Faculty of Technical Sciences Universitaet Klagenfurt, A9020 Klagenfurt, Austria"}]},{"given":"Ahmad","family":"Haj Mosa","sequence":"additional","affiliation":[{"name":"Institute for Smart Systems Technologies, Faculty of Technical Sciences Universitaet Klagenfurt, A9020 Klagenfurt, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1239-9261","authenticated-orcid":false,"given":"Fadi Al","family":"Machot","sequence":"additional","affiliation":[{"name":"Research Center Borstel\u2014Leibniz Lung Center, 23845 Borstel, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5675-4747","authenticated-orcid":false,"given":"Jean Chamberlain","family":"Chedjou","sequence":"additional","affiliation":[{"name":"Research Center Borstel\u2014Leibniz Lung Center, 23845 Borstel, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tavakkoli, V., Mohsenzadegan, K., and Kyamakya, K. (2020). A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction. Sensors, 20.","DOI":"10.3390\/s20195672"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dai, Y., Fu, Y., Li, B., Zhang, X., Yu, T., and Wang, W. (2019). A New Filtering System for Using a Consumer Depth Camera at Close Range. Sensors, 19.","DOI":"10.3390\/s19163460"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Michalak, H., and Okarma, K. (2020). Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition. Sensors, 20.","DOI":"10.3390\/s20102914"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Khan, Z., Shafait, F., and Mian, A. (2019). Converting a Common Low-Cost Document Scanner into a Multispectral Scanner. Sensors, 19.","DOI":"10.3390\/s19143199"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lin, J., Yang, H., Wang, H., Bai, T., Liu, Q., and Pang, Y. (2020). An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors. Sensors, 20.","DOI":"10.3390\/s20102942"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nasir, I.M., Khan, M.A., Yasmin, M., Shah, J.H., Gabryel, M., Scherer, R., and Dama\u0161evi\u010dius, R. (2020). Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training. Sensors, 20.","DOI":"10.3390\/s20236793"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nagaoka, Y., Miyazaki, T., Sugaya, Y., and Omachi, S. (2021). Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale. Sensors, 21.","DOI":"10.3390\/s21041232"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, X., Li, H., Zhang, Z., and Gan, C. (2019). The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data. Sensors, 19.","DOI":"10.3390\/s19040809"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T., Chagas, P., Alves, J., Santos, C., Sousa Santos, B., and Serique Meiguins, B. (2020). A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction. Sensors, 20.","DOI":"10.3390\/s20164370"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:52:45Z","timestamp":1760165565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,30]]},"references-count":9,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21175849"],"URL":"https:\/\/doi.org\/10.3390\/s21175849","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,8,30]]}}}