Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model

Abstract

A pioneer conceptual combination of level set method and Gaussian Mixture Model (GMM) is presented in the described multilingual, arbitrary-oriented character segmentation. The method is a serial accomplishment processes of Gaussian low pass filter, single level 2 Dimensional Discrete Wavelet Transform (2D DWT) for better feature extraction and implemented level set method, k-means clustering algorithm with GMM to achieve a veracious character segmentation results by detecting great measure of true text region of an image. The proposed method segments a character chiefly by distinguishing the touching character constituents and also deals discontinuities presence in a character by using Laplacian of Gaussian filter and morphological bridge function in a intellectual way. The exhibited method was explored on Multi-script Robust Reading Competition dataset and on our privately collected graphical and handwritten multilingual, arbitrarily-oriented text images. The suggested method is compared with the well known multilingual and arbitrarily-oriented charter segmentation methods. The described method attains better segmentation outcomes when compared to the familiar functioning methods. Hence, the suggested method is highly suitable to consider as an improved, standard and procedural technique.