{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:35:40Z","timestamp":1772724940643,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:00:00Z","timestamp":1667865600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2022,11,8]]},"abstract":"<jats:p>Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Geez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Geez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this work, the proposed model is to recognize Geez digits using CNN. Deep neural networks, which have recently shown exceptional performance in numerous pattern recognition and machine learning applications, are used to recognize handwritten Geez digits, but this has not been attempted for Ethiopic scripts. Our dataset, which contains 51,952 images of handwritten Geez digits collected from 524 individuals, is used to train and evaluate the CNN model. The application of the CNN improves the performance of several machine-learning classification methods significantly. Our proposed CNN model has an accuracy of 96.21% and a loss of 0.2013. In comparison to earlier research works on Geez handwritten digit recognition, the study was able to attain higher recognition accuracy using the developed CNN model.<\/jats:p>","DOI":"10.1155\/2022\/8515810","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T18:53:14Z","timestamp":1667933594000},"page":"1-12","source":"Crossref","is-referenced-by-count":13,"title":["Handwritten Geez Digit Recognition Using Deep Learning"],"prefix":"10.1155","volume":"2022","author":[{"given":"Mukerem","family":"Ali Nur","sequence":"first","affiliation":[{"name":"Adama Science and Technology University, Adama, Ethiopia"}]},{"given":"Mesfin","family":"Abebe","sequence":"additional","affiliation":[{"name":"Adama Science and Technology University, Adama, Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7486-5259","authenticated-orcid":true,"given":"Rajesh Sharma","family":"Rajendran","sequence":"additional","affiliation":[{"name":"Adama Science and Technology University, Adama, Ethiopia"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-019-1340-4"},{"key":"2","article-title":"Handwritten Amharic Character Recognition Using a Convolutional Neural Network","author":"M. S. Gondere","year":"2019"},{"key":"3","article-title":"Handwritten and machine printed ocr for geez numbers using artificial neural network","author":"E. G. Beyene","year":"2019"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging4010015"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100182"},{"key":"6","unstructured":"ElitezO.Handwritten digit string segmentation and recognition using deep learning2015Ankara, TurkeyMiddle East Technical UniversityMaster\u2019s Thesis"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/s10032-012-0185-9"},{"key":"8","first-page":"1","article-title":"Recognizing handwritten single digits and digit strings using deep architecture of neural networks","author":"R. Saabni"},{"key":"9","unstructured":"ShiZ.DateF.Detecting date regions on handwritten document images based on positional expectancy2016Groningen, NetherlandsUniversity of GroningenMaster\u2019s Thesis"},{"key":"10","first-page":"225","article-title":"Avoiding segmentation in multi-digit numeral string recognition by combining single and two-digit classifiers trained without negative examples","author":"D. Ciresan"},{"issue":"7","key":"11","first-page":"990","article-title":"Handwritten digit recognition using deep learning","volume":"6","author":"A. Dutt","year":"2017","journal-title":"International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)"},{"key":"12","first-page":"541","article-title":"Recognition of handwritten digit using convolutional neural network in python with tensorflow and comparison of performance for various hidden layers","author":"F. Siddique"},{"key":"13","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"I. S. Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"14","article-title":"LeNet-5, convolutional neural networks","author":"Y. LeCun","year":"2015"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"16","first-page":"274","article-title":"Offline handwritten digits recognition using machine learning","author":"S. 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