The objective of this study is to propose a new method for text region localization and character extraction in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts multilingual text from natural scene image with cluttered backgrounds. The proposed approach involves four steps. First, potential text regions in an image are extracted based on edge features using Contourlet transform. In the second step, potential text regions are tested for text content or non-text using GLCM features and SVM classifier. In the third step, detection of multiple lines in localized text regions is done and line segmentation is performed using horizontal profiles. In the last step, each character of the segmented line is extracted using vertical profiles. The experimentation has been done using images drawn from own dataset and ICDAR dataset. The performance is measured in terms of the precision and recall. The results demonstrate the effectiveness of the proposed method, which can be used as an efficient method for text recognition in natural scene images.

Accurate detection of exudates in the diabetic retinal images is a challenging task. The images can have varying contrast and color characteristics. In this paper authors present the performance comparison of two feature extraction methods namely color intensity features and second order texture features based on GLCM. Authors have proposed and implemented new approach for GLCM feature calculation in which the input image is divided into number smaller blocks and GLCM features are computed on these blocks. The performance of each feature extraction method is evaluated using Back Propagation Neural Network (BPNN) classifier that is classifying the blocks as either abnormal block or normal block. With GLCM features, an accuracy of 76.6% was obtained and with color features an accuracy of 100% was obtained. It was found that color features are better in identifying true positives than GLCM based texture features. However use of GLCM features reduces the occurrence of false positives.