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In order to achieve high-precision handwritten digit recognition, this study proposes a high-performance model based on convolutional neural networks, which extracts features through a convolutional layer, under-samples using a maximum pooling layer to retain important features, and further extracts and classifies the features through a fully connected layer, and ultimately outputs the classified probability distributions using softmax functions. We compare the three optimization algorithms: experimental results show that the RMSprop optimizer has a training accuracy of more than 99% on the MNIST dataset, with a loss rate close to zero, whereas SGD and Adam, although slightly inferior in terms of performance, still have good recognition results, which verifies the superiority of CNNs combined with the RMSprop optimizer in the handwritten digit recognition task.<\/jats:p>","DOI":"10.1177\/14727978241302442","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:15:24Z","timestamp":1745896524000},"page":"1667-1677","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Research and application of constructing image recognition models for archival handwriting"],"prefix":"10.1177","volume":"25","author":[{"given":"Yuanyuan","family":"Kong","sequence":"first","affiliation":[{"name":"Lianyungang Technical College, Lianyungang, China"}]}],"member":"179","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1848\/1\/012015"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"issue":"4","key":"e_1_3_2_4_2","article-title":"Image manipulation detection using the attention mechanism and faster R-CNN","volume":"50","author":"Tan K","year":"2023","unstructured":"Tan K, Li L, Huang Q. 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