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It is a challenging problem that has received a lot of attention in the past three decades. Research has focused on the recognition of Latin languages\u2019 handwriting. Fewer studies have been done for the Arabic language. In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7\u201312 which we call Hijja. Our dataset contains 47,434 characters written by 591 participants. In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN). We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset. Results show that our model\u2019s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature.<\/jats:p>","DOI":"10.1007\/s00521-020-05070-8","type":"journal-article","created":{"date-parts":[[2020,6,28]],"date-time":"2020-06-28T02:02:35Z","timestamp":1593309755000},"page":"2249-2261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":177,"title":["Arabic handwriting recognition system using convolutional neural network"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-1886","authenticated-orcid":false,"given":"Najwa","family":"Altwaijry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0550-5115","authenticated-orcid":false,"given":"Isra","family":"Al-Turaiki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,28]]},"reference":[{"key":"5070_CR1","doi-asserted-by":"crossref","unstructured":"Abdalkafor AS (2018) Survey for databases on Arabic off-line handwritten characters recognition system. 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