2 April 2018 Extreme learning machine for handwritten Indic script identification in multiscript documents
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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.
© 2018 SPIE and IS&T
Sk. Md. Obaidullah, Sk. Md. Obaidullah, Amitava Bose, Amitava Bose, Himadri Mukherjee, Himadri Mukherjee, KC Santosh, KC Santosh, Nibaran Das, Nibaran Das, Kaushik Roy, 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 . Submission: Received: 15 January 2018; Accepted: 13 March 2018
Received: 15 January 2018; Accepted: 13 March 2018; Published: 2 April 2018
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