Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation

Susanto A.; Mulyono I.U.W.; Sari C.A.Rachmawanto E.H.Setiadi D.R.I.M.Sarker M.K.

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Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation

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Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65 accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

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来源
《IAES International Journal of Artificial Intelligence》 |2023年第3期|1448-1458|共11页
作者
Susanto A.; Mulyono I.U.W.; Sari C.A.Rachmawanto E.H.Setiadi D.R.I.M.Sarker M.K.;
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作者单位

Department of Informatics Engineering, Dian Nuswantoro University;

Department of Computing Science, University of Hartford;

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正文语种英语
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关键词
Convolution neural network; Data augmentation; Fourth; Javanese script recognition; Small dataset;

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Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation

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