Removing Noise from Handwritten Character Images using U-Net

Abstract

Offline handwritten character recognition still remains a tough challenge for AI techniques and algorithms. This is because handwritten documents frequently introduce some amount of noise in the images during the scanning procedures. The presence of noise in the scanned images make them murky and/or blurred and therefore hard to read. In this study, we tried using the CNN architecture named “U-Net” to analyze 607,200 sample images consisting of 3,036 Japanese characters. Our results indicate that the “U-Net” has sufficient ability to get rid of noise from characters and enhance the parts of strokes even though there are a huge variety of handwritten styles.