Character segmentation from multi-oriented video words using Discrete Wavelet Transform and K-Means Clustering

Abstract

The paper presents a two stage methods for the image segmentation from multi-oriented video words by using of DWT process k-means clustering.  As the commercial usage of digital contents are on rise, the requirement of an efficient and error free indexing text along with text localization and extraction is of high importance. Majority of the previous research work on text extraction has focused on scene text, uniform background, and extensive use of wavelet domain and frequent usage of only grey-scale image as input. The data has been taken from a  d.w.t for compression of the data from the output of gray scale . The proposed system has broader scale of consideration of input image with much complicated backgrounds along with consideration of sliding windows. For much accuracy, morphological operation is included to accurately distinguish the text and non-text area for better text localization and extraction. The experimental result was compared with all the prior significant work in text extraction where the results show a much robust, efficient, and much accurate text extraction technique.