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.

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The objective of this paper is to propose a novel approach to recognize Characters (Digits, Upper case letters and Lower case letters) of text spotted in natural scene image with cluttered backgrounds. The projection profile method is used to segment localized text component into individual characters, which is then submitted to character recognition process. In the proposed methodology, the Moment Invariants (MI) features are extracted from the Contourlet transformed image of segmented input characters. The PNN classifier is used for character recognition and its performance is compared with KNN classifier. The testing has been done using images taken from ICDAR dataset and personal dataset. The performance is measured in terms of recognition accuracy. The experimental results demonstrated the efficiency of the proposed method