Color K-means, Gaussian Filter and Aperture Concept for Text Localization in Images

Text extraction in image is an essential role in machine learning and computer vision field. The text localization process determines the presence and location of text in the given inputs. The challenges, which are occurred during the text localization task is different orientation, low-resolution, complex background and illumination with variation in font size and color. In this research paper, the color k-means is applied to make separate group of colors present in the text image. Histogram equalization process is applied to sharpen the text pixels from the background pixels. The Gaussian filter is applied to combine all text pixels together. Aperture concept has implemented to locate the actual text regions. The standard datasets like hua’s and nus dataset were used to estimate the performance of the presented model. Precision, recall and f-measure is used to estimate the performance of the proposed model.