  In this paper, we introduce Handwritten augmentation, a new data augmentation
for handwritten character images. This method focuses on augmenting handwritten
image data by altering the shape of input characters in training. The proposed
handwritten augmentation is similar to position augmentation, color
augmentation for images but a deeper focus on handwritten characters.
Handwritten augmentation is data-driven, easy to implement, and can be
integrated with CNN-based optical character recognition models. Handwritten
augmentation can be implemented along with commonly used data augmentation
techniques such as cropping, rotating, and yields better performance of models
for handwritten image datasets developed using optical character recognition
methods.
