Compared to derivatives from Latin script, recognition of derivatives from Arabic handwritten script is a complex task due to the presence of two-dimensional structure, context-dependent shape of characters, high number of ligatures, overlap of characters, and placement of diacritics. While significant attempts exist for Latin and Arabic scripts, very few attempts have been made for offline, handwritten, Urdu script. In this paper, we introduce a large, annotated dataset of handwritten Urdu sentences. We also present a methodology for the recognition of offline handwritten Urdu text lines. A deep learning based encoder /decoder framework with attention mechanism is used to handle two-dimensional text structure. While existing approaches report only character level accuracy, the proposed model improves on BLSTM-based state-of-the-art by a factor of 2 in terms of character level accuracy and by a factor of 37 in terms of word level accuracy. Incorporation of attention before a recurrent decoding framework helps the model in looking at appropriate locations before classifying the next character and therefore results in a higher word level accuracy.
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