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

[wanghaisheng]

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122597/

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.

[wanghaisheng]

MDLSTM based Urdu character recognition system After choosing suitable parameters, the image of a text line is processed by dividing it
into small patches using input blocks having width of 1 column and height of 4 rows. The raw pixels of the image are collapsed to a vector of length 4 and are fed to the MDLSTM with the corresponding ground truth. The small patches of the image are then scanned through forward and backward passes in all four directions (horizontally and vertically) by MDLSTM to extract and learn distinct features. The detailed schema of implementation of MDLSTM is shown in Fig. 7

[wanghaisheng]

40064_2016_Article_3442.pdf