Handwritten image augmentation
This is an incremental improvement for optical character recognition systems handling handwritten images.
The paper tackles the problem of limited data for handwritten character recognition by introducing a new data augmentation method that alters character shapes, which improves model performance when combined with existing techniques.
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.