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29 October 2018 Removing noise from handwritten character images using U-Net through online learning
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Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360K (2018) https://doi.org/10.1117/12.2514045
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China
Abstract
Today, recognizing offline handwritten character images is still hard challenge. This is because there are the obstacles, ‘noise’ produced in scanning process. Noise makes handwritten character distorted, murky, and blurred. As a result, it become hard to read and recognize these images for human. In this study, we tried to get rid of various noises using CNN architecture named “U-Net” to analyze 607,200 sample images consisting of 3,036 Japanese characters. Finally, our results indicate that the “U-Net” has efficient ability to remove noise and enhance the parts of strokes even through there are a huge variety of handwritten styles which includes various noises.
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Rina Komatsu and Tad Gonsalves "Removing noise from handwritten character images using U-Net through online learning", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360K (29 October 2018); https://doi.org/10.1117/12.2514045
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