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

• Main Authors: Boseon Hong, Bongjae Kim
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Sensors
• Subjects: convolutional neural networks; mobile services; Caoshu recognition; model optimization; data augmentation
• Online Access: https://www.mdpi.com/1424-8220/20/16/4641

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.