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Text in camera captured images contains important and useful information. Text in images can be used for identification, indexing and retrieval. Detection and localization of text from camera captured images is still a challenging task due to high variability of text appearance. In this paper we propose an efficient algorithm, for detecting and localizing text in natural scene images. The method is based on texture feature extraction using first and second order statistics. The entire work is divided into two stages. Text regions are detected in the first stage using texture features. Discriminative functions are used to filter out non-text regions. In the second stage the detected text regions are merged and localized. An experimental results obtained shows that the proposed approach detects and localizes texts of various sizes, fonts, orientations and languages efficiently.

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<span lang="EN-US">In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.</span>

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Text Extraction from natural scene images has been done with various methodologies. Most of the existing systems mainly use color and edges for detecting the text. We propose a two stage hybrid text extraction approach by combining texture and CC-based information. Text in the image is detected and localized using first and second order statistical texture features. In the next stage CC extraction is used to segment candidate text components from the localized text region. Finally morphological operations and heuristic filters are used to filter out non text components. Experimental results show that the proposed approach detects, localizes and extracts text from natural scene images efficiently and also can handle variations in size, fonts and orientation.