Dimensionality Reduction And Feature Selection Methods For Script Identification On Document Images
Bruce Poon, Saami Rahman, M. Ashraful Amin, Saami Rahman, M. Ashraful Amin · Zenodo (CERN European Organization for Nuclear Research) · 2018
Abstract The goal of this research is to explore effects of dimensionality reduction and feature selection on the problem of script identification from images of printed documents. The kadjacent segment is ideal for this use due to its ability to capture visual patterns. We have used principle component analysis to reduce the size of our feature matrix to a handier size that can be trained easily, and experimented by including varying combinations of dimensions of the super feature set. A modular approach in neural network was used to classify 7 languages – Arabic, Chinese, English, Japanese, Tamil, Thai and Korean. Keywords Feature reduction; feature selection; neural networks; principle component analysis; script identification For More Details: http://it-in-industry.com/itii_papers/2014/2114itii01.pdf