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    Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network by Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Dost Muhammad Khan, Mickäel Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Hizbullah Khattak, Sikandar Ali, Ali Samad

    Published 2021-01-01
    “…We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). …”
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