Extreme learning machine for handwritten Indic script identification in multiscript documents

Sk. Md. Obaidullah; Amitava Bose; Himadri Mukherjee; KC Santosh; Nibaran Das; Kaushik Roy

doi:10.1117/1.JEI.27.5.051214

2 April 2018 Extreme learning machine for handwritten Indic script identification in multiscript documents

Sk. Md. Obaidullah, Amitava Bose, Himadri Mukherjee, KC Santosh, Nibaran Das, Kaushik Roy

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 051214 (April 2018). https://doi.org/10.1117/1.JEI.27.5.051214

Abstract

Unlike the conventional feedforward neural network, an emergent learning technique—which we call extreme learning machine (ELM)—provides a generalized performance of neural network with less user intervention and comparatively faster training. We study ELM with five different activation functions, sigmoidal, sine, hard limiter, triangular basis, and radial basis, for handwritten Indic script identification in multiscript documents. To describe scripts, both script dependent and independent features are computed. For validation, a dataset of 3300 handwritten line-level document images (300 samples per script) of 11 official Indic scripts is used. In our study, we observe that the sigmoidal activation function performs the best regardless of the number of scripts used, i.e., script identification cases: biscript, triscript, and multiscript.

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Sk. Md. Obaidullah, Amitava Bose, Himadri Mukherjee, KC Santosh, Nibaran Das, and Kaushik Roy "Extreme learning machine for handwritten Indic script identification in multiscript documents," Journal of Electronic Imaging 27(5), 051214 (2 April 2018). https://doi.org/10.1117/1.JEI.27.5.051214

Received: 15 January 2018; Accepted: 13 March 2018; Published: 2 April 2018

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