Recognition of printed Urdu ligatures using convolutional neural networks

We present a holistic technique for recognition of text in cursive scripts using printed Urdu ligatures as a case study. Convolutional neural networks (CNNs) are trained on high-frequency ligature clusters for feature extraction and classification. A query ligature presented to the system is first divided into primary and secondary ligatures that are separately recognized and later associated in a postprocessing step to recognize the complete ligature. Experiments are carried out using transfer learning on pretrained networks as well as by training a network from scratch. The technique is evaluated on ligatures extracted from two standard databases of printed Urdu text, Urdu printed text image (UPTI) and Center of Language Engineering (CLE), as well as by combining the ligatures of the two datasets. The system realizes high recognition rates of 97.81% and 89.20% on the UPTI and CLE databases, respectively.