Text Extraction from Live Captured Image with Diversified Background using Edge Based & K- Means Clustering
Anuj Kumar Singh, Preeti Singh · 2014
The proposed system highlights a novel approach of extracting a text from image using K-Means Clustering. Text Extraction from image is concerned with extracting the relevant text data from a collection of images. Recent studies in the field of image processing show a great amount of interest in content retrieval from images and videos. This content can be in the form of objects, color, texture, shape as well as the relationships between them. 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. 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.