Improving Handwritten Arabic Text Recognition Using an Adaptive Data-Augmentation Algorithm

Deep learning has increased the performance of classification and object detection, but it generally requires large amounts of labeled data for training. In this paper, we introduce a new data augmentation algorithm that promotes diversity between classes, representing the characters of the Arabic script, and can balance samples between different classes. This algorithm gives each word in the lexicon a weight. The weight of a word is based on the occurrence probabilities of the characters constituting the word. Minority classes are given higher weight as compared to the classes frequently occurring in the text. The data augmentation technique was evaluated on a handwritten word recognition task using the publicly available IFN/ENIT and AHDB datasets. We see significant improvement in results by employing our data augmentation technique, and we achieve state-of-the-art results on both datasets.