Abstract
In recent years, there has been a growing need for techniques that enable users to explain the basis for decisions made by neural networks in image recognition problems. While many conventional methods have focused on presenting the spatial basis for pattern recognition judgments by identifying the regions in the image that significantly influence the recognition results, our proposed method focuses on presenting a structural basis for recognizing handwritten Kanji characters. This is accomplished by pruning neural networks to acquire detectors for simple patterns that are commonly found in Kanji characters. During the process of sequentially adding these detectors, we also apply pruning to the network connecting the detectors, thereby aiming to precisely acquire simple hierarchical connections among the detectors as a structural recognition process in pattern recognition. We successfully applied this method to simple handwritten Kanji images, and achieved Kanji recognition by combining detectors for simple patterns without significantly affecting the recognition rate.
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Acknowledgements
This work was supported by JSPS KAKENHI (Grant Number JP 19K12045).
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Gyohten, K., Ohki, H., Takami, T. (2023). Character Structure Analysis by Adding and Pruning Neural Networks in Handwritten Kanji Recognition. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14407. Springer, Cham. https://doi.org/10.1007/978-3-031-47637-2_10
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DOI: https://doi.org/10.1007/978-3-031-47637-2_10
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