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CArDIS: A Swedish Historical Handwritten Character and Word Dataset
KTO Karatay Univ, TUR.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7536-3349
Univ Witwatersrand, ZAF.
Blekinge Institute of Technology. student.
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 55338-55349Article in journal (Refereed) Published
Abstract [en]

This paper introduces a new publicly available image-based Swedish historical handwritten character and word dataset named Character Arkiv Digital Sweden (CArDIS) (https://cardisdataset.github.io/CARDIS/). The samples in CArDIS are collected from 64, 084 Swedish historical documents written by several anonymous priests between 1800 and 1900. The dataset contains 116, 000 Swedish alphabet images in RGB color space with 29 classes, whereas the word dataset contains 30, 000 image samples of ten popular Swedish names as well as 1, 000 region names in Sweden. To examine the performance of different machine learning classifiers on CArDIS dataset, three different experiments are conducted. In the first experiment, classifiers such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbor (k-NN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Random Forest (RF) are trained on existing character datasets which are Extended Modified National Institute of Standards and Technology (EMNIST), IAM and CVL and tested on CArDIS dataset. In the second and third experiments, the same classifiers as well as two pre-trained VGG-16 and VGG-19 classifiers are trained and tested on CArDIS character and word datasets. The experiments show that the machine learning methods trained on existing handwritten character datasets struggle to recognize characters efficiently on the CArDIS dataset, proving that characters in the CArDIS contain unique features and characteristics. Moreover, in the last two experiments, the deep learning-based classifiers provide the best recognition rates.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2022. Vol. 10, p. 55338-55349
Keywords [en]
Character recognition, Optical character recognition software, Feature extraction, Hidden Markov models, Handwriting recognition, Machine learning, Image recognition, Character and word recognition, machine learning methods, optical character recognition (OCR), old handwritten style, Swedish handwritten character dataset, Swedish handwritten word dataset
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-23171DOI: 10.1109/ACCESS.2022.3175197ISI: 000804633200001OAI: oai:DiVA.org:bth-23171DiVA, id: diva2:1670902
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032
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open access

Available from: 2022-06-16 Created: 2022-06-16 Last updated: 2022-06-16Bibliographically approved

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fulltext(2364 kB)430 downloads
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Kusetogullari, Hüseyin

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