Abstract
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.
Highlights
Character recognition is a technique in computer vision that recognizes and classifies the regions of letters and digits to be identified inside a single image
There is a reduction in the recognition rate when low-quality legacy content is given as an input to a general character recognition neural network owing to the disparity in the resolution of the training data and the input image
For effective performance of the collaborative learning, first, the SR and the character recognition neural networks were separately trained with the same 11,428 training set, and their performance was verified with the 1,999 samples in the validation set
Summary
Character recognition is a technique in computer vision that recognizes and classifies the regions of letters and digits to be identified inside a single image. A character recognition neural network detects a character area in an input image, detects the area, and adjusts its size according to the input In this process, the quality of the output data deteriorates owing to the loss of resolution of the character area while cropping. There is a reduction in the recognition rate when low-quality legacy content is given as an input to a general character recognition neural network owing to the disparity in the resolution of the training data and the input image. There is a lack of practical research on improving the quality of low-quality legacy content and optimizing character recognition simultaneously through collaborative learning of the SR and character recognition neural networks. The proposed alternative collaborative learning algorithm can be used to dramatically improve the recognition rate in various existing character recognition fields
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