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Open AccessArticle

Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization

by Boseon Hong 1,2 and Bongjae Kim 3,*
1
Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan 31460, Korea
2
Artificial Intelligence Research Center, Korea Electronics Technology Institute, Seongnam 13488, Korea
3
Division of Computer Science and Engineering, Sun Moon University, Asan 31460, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4641; https://doi.org/10.3390/s20164641
Received: 20 July 2020 / Revised: 13 August 2020 / Accepted: 17 August 2020 / Published: 18 August 2020
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed scheme, an image pre-processing and data augmentation techniques for our Caoshu dataset were applied to optimize and enhance the CNN-based Caoshu character recognition model’s recognition performance. In the performance evaluation, Caoshu character recognition performance was compared and analyzed according to the proposed performance optimization. Based on the model validation results, the recognition accuracy was up to about 98.0% in the case of TOP-1. Based on the testing results of the optimized model, the accuracy, precision, recall, and F1 score are 88.12%, 81.84%, 84.20%, and 83.0%, respectively. Finally, we have designed and implemented a Caoshu recognition service as an Android application based on the optimized CNN based Cahosu recognition model. We have verified that the Caoshu recognition service could be performed in real-time. View Full-Text
Keywords: convolutional neural networks; mobile services; Caoshu recognition; model optimization; data augmentation convolutional neural networks; mobile services; Caoshu recognition; model optimization; data augmentation
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Hong, B.; Kim, B. Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization. Sensors 2020, 20, 4641.

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