Arif Iqbal Umar

TL;DR: This work presents a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script using the proposed hierarchical combination of CNN and MDLSTM.

TL;DR: An implicit segmentation based recognition system for Urdu text lines in Nastaliq script that relies on sliding overlapped windows on lines of text and extracting a set of statistical features is presented.

TL;DR: A robust feature extraction approach that extracts feature based on right-to-left sliding window that significantly reduce the label error for Urdu Nasta’liq text lines and outperforms the state-of-the-art results.

TL;DR: In this paper, the authors survey the existing studies which optimize the task offloading in edge networks with mobility management and compare the listed state-of-the-art research works based on the components identified from taxonomy.

TL;DR: 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 achieves a recognition accuracy of 98% for the unconstrained Urdu Nasta'liq printed text, which significantly outperforms the state-of-the-art techniques.