Overlapping Character Recognition for Handwritten Text Using Discriminant Hidden Semi-Markov Model

Shinde, Ashish; Shinde, Abhijit

doi:10.1007/978-981-10-7245-1_17

Cited by 3 publications

(1 citation statement)

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“…The source language can be represented either in its spatial (offline) form [1] or temporal (online) [2] form in graphical marks. In-depth analysis of handwritten text gives rise to a number of useful applications such as author profiling [3], named entity recognition [4], and overlapped characters recognition [5].…”

Section: Introductionmentioning

confidence: 99%

Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

et al. 2019

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We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.

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