Urdu Nasta’liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networksReport as inadecuate




Urdu Nasta’liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks - Download this document for free, or read online. Document in PDF available to download.

SpringerPlus

, 5:2010

Computer Science

Abstract

The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta’liq writing style. Nasta’liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition. We present Multi-dimensional Long Short Term Memory MDLSTM Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta’liq writing style. Experiments show that MDLSTM attained a recognition accuracy of 98% for the unconstrained Urdu Nasta’liq printed text, which significantly outperforms the state-of-the-art techniques.

KeywordsUrdu OCR BLSTM MDLSTM CTC  Download fulltext PDF



Author: Saeeda Naz - Arif Iqbal Umar - Riaz Ahmed - Muhammad Imran Razzak - Sheikh Faisal Rashid - Faisal Shafait

Source: https://link.springer.com/







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