Abstract
Text recognition from images can substantially facilitate a wide range of applications. However, screen-rendered images pose great challenges to current methods due to its low resolution and low signal to noise ratio properties. This paper proposed a Chinese characters recognition model using inception module based convolutional neural networks. Chinese characters were firstly extracted using vertical projection and error correction; then it can be recognized via inception module based convolutional neural networks. The proposed model can effectively segment Chinese characters from screen-rendered images, and significantly reduce the training time. Extensive experiments have been conducted on a number of screen-rendered images to evaluate the performance of the proposed model against state-of-the-art models.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (61602349, 61440016, 61373109) and the Hubei Chengguang Talented Youth Development Foundation (2015B22).
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Xu, X., Zhou, J., Zhang, H., Fu, X. (2018). Chinese Characters Recognition from Screen-Rendered Images Using Inception Deep Learning Architecture. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_69
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DOI: https://doi.org/10.1007/978-3-319-77380-3_69
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