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Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Shortdoi:10.1109/icdar.2013.212 dblp:conf/icdar/Ul-HasanARSB13 fatcat:dxbu2g2udrb5vnsodibzma5sky