Methods of data augmentation for palimpsest character recognition with Deep Neural Network.

Deep Neural Networks (DNN) are now widely used in computer vision, due to their recent success in large-scale image classification. Despite the impressive performance of DNN in supervised optical character recognition (OCR) for handwritten documents, they typically have not been used for the application of reading historical manuscripts. The main reason is the lack of availability of large datasets and of understanding about the amount of data required to achieve good results in supervised character classification tasks. In this paper, we show how to train DNN with a limited amount of data for the application of OCR of historical manuscripts. We investigate different strategies for data augmentation for palimpsests and analyze the effect of different methods on DNN performance. The method was tested on the well-known Archimedes palimpsest image dataset using 30 exemplars per character. We found that using the random mask overlay method of augmentation we can achieve up to 82% accuracy for classifying characters of the Archimedes palimpsest.