An improved and efficient image mining technique for classification of textual images using low-level image features

This paper proposes a system that classifies textual images (images that encounter text within) using low-level image features. Image classification and content based image retrieval is a growing field in the area of image classification. In this paper, the approach is based on various low-level image features including GLCM features like mean, skewness, energy, contrast, homogeneity. Using these various features, the differences between images are measured, and then these are used to classify the textual images by performing classification and clustering techniques on datasets. The proposed method experimented on 60 different textual images to obtain an improved result that was not obtained in earlier systems along with classification of images in three main categories: document, scene and caption.