Generative adversarial network based adaptive data augmentation for handwritten Arabic text recognition.

ABSTRACT: Training deep learning based handwritten text recognition systems needs a lot of data in terms of text images and their corresponding annotations. One way to deal with this issue is to use data augmentation techniques to increase the amount of training data. Generative Adversarial Networks (GANs) based data augmentation techniques are popular in literature especially in tasks related to images. However, specific challenges need to be addressed in order to effectively use GANs for data augmentation in the domain of text recognition. Text data is inherently imbalanced in terms of frequency of different characters appearing in training samples and the training data as a whole. GANs trained on the imbalanced dataset leads to augmented data that does not represent the minority characters well.

SUBMITTER: Eltay M 

PROVIDER: S-EPMC8802770 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature