| Summary: | Character recognition in a single image is a technology utilized in various sensor platforms, such as smart parking and text-to-speech systems, and numerous studies are being conducted to improve its performance by experimenting with novel approaches. However, when low-quality images were inputted to a character recognition neural network for recognition, a difference in the resolution of the training image and low-quality image results in poor accuracy. To resolve this problem, this study proposes a collaborative trainable mechanism that integrates a global image feature extraction-based super-resolution neural network with a character recognition neural network. This collaborative trainable mechanism helps the character recognizer to be robust to inputs with varying quality in the real world. The alternative collaborative learning and character recognition performance test was conducted using the license plate image dataset among various character images, and the effectiveness of the proposed algorithm was verified using a performance test.
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