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The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red\u2013green\u2013blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of machine learning methods on several digit datasets is carried out. Additionally, correlation between ARDIS and existing digit datasets Modified National Institute of Standards and Technology (MNIST) and US Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset. Accordingly, convolutional neural network trained on MNIST and USPS and tested on ARDIS provide the highest accuracies $$58.80\\%$$<\/jats:tex-math>\n \n 58.80<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $$35.44\\%$$<\/jats:tex-math>\n \n 35.44<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, respectively. Consequently, the results reveal that machine learning methods trained on existing datasets can have difficulties to recognize digits effectively on our dataset which proves that ARDIS dataset has unique characteristics. This dataset is publicly available for the research community to further advance handwritten digit recognition algorithms.<\/jats:p>","DOI":"10.1007\/s00521-019-04163-3","type":"journal-article","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T17:04:23Z","timestamp":1553879063000},"page":"16505-16518","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["ARDIS: a Swedish historical handwritten digit dataset"],"prefix":"10.1007","volume":"32","author":[{"given":"Huseyin","family":"Kusetogullari","sequence":"first","affiliation":[]},{"given":"Amir","family":"Yavariabdi","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4390-411X","authenticated-orcid":false,"given":"Abbas","family":"Cheddad","sequence":"additional","affiliation":[]},{"given":"H\u00e5kan","family":"Grahn","sequence":"additional","affiliation":[]},{"given":"Johan","family":"Hall","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,29]]},"reference":[{"key":"4163_CR1","doi-asserted-by":"crossref","unstructured":"Djeddi C, Al-Maadeed S, Gattal A, Siddiqi I, Ennaji A, Abed HE (2016) ICFHR2016 competition on multi-script writer demographics classification using \u201cQUWI\u201d database. 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