Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book. Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy. Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy. The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class. The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.