International Journal of Scientific & Engineering Research Volume 2, Issue 12, December-2011 1 ISSN 2229-5518 IJSER © 2011 http://www.ijser.org Character Localization From Natural Images Using Nearest Neighbours Approach Shaila Chugh, Yogendra Kumar Jain Abstract— Scene text contains significant and beneficial information. Extraction and localization of scene text is used in many applications. In this paper, we propose a connected component based method to extract text from natural images. The proposed method uses color space processing. Histogram analysis and geometrical properties are used for edge detection. Character recognition is done through OCR which accepts the input in form of text boxes, which are generated through text detection and localization stages. Proposed method is robust with respect to the font size, color, orientation, and style. Results of the proposed algorithm, by taking real scenes, including indoor and outdoor images, show that this method efficiently extract and localize the scene text. Index Terms— Character Localization, Scene Text, Nearest Neighbours, Edge Detection, OCR, Histogram, Filters. ——————————  —————————— 1 INTRODUCTION EXT detection and localization from natural scene is an active research area in computer vision field. Scene text appear as a part of scene, such as text in vehicle number plates, hoardings, books, CD covers, etc. Various font sizes and styles, orientations, alignment, ef- fects of uncontrolled illumination, reflections, shadows, the distortion due to perspective projection as well as the com- plexity of image backgrounds, makes automatic text localiza- tion and extraction scene a challenging problem. Localization of characters in images is used in many applications. Text de- tection can be used in the applications of page segmentation, document retrieving, address block location, etc. For extrac- tion of text, different approaches have been suggested, based on the text characteristics. The method proposed by Xiaoqing Liu et al. [2] is based on the fact that edges are a reliable feature of text, regardless of color/intensity, layout, orientations, etc. Edge strength, density and the orientation variance are three distinguishing characteristics of text embedded in images, which can be used as main features for detecting scene text. Their proposed me- thod consists of three stages: target text area detection, text area localization and character extraction. Wang et al. [3] proposed a connected-component based method which combines color clustering, a black adjacency graph (BAG), an aligning-and-merging-analysis scheme and a set of heuristic rules together to detect text in the application of sign recognition such as street indicators and billboards. Author has mentioned that uneven reflections have resulted in incomplete character segmentation that increased the false alarm rate. Kim et al. [4] implemented a hierarchical feature combination method to implement text detection in real scenes. However, authors admit that their proposed method could not handle large text very well due to the use of local features that represents only local variations of image blocks. Gao et al. [5] developed a three layer hierarchical adaptive text detection algorithm for natural scenes. It has been applied in prototype Chinese sign translation system which mostly has a horizontal and/or vertical alignment. Cai et al. [7] have proposed a method that detects both low and high contrast texts without being affected by language and font-size. Their algorithm first converts the video image into an edge map using color edge detector [9] and uses a low global threshold to filter out definitely non-edge points. Then, a selective local thresholding is performed to simplify the complex background, then the edge-strength smoothing oper- ator and an edge-clustering power operator highlights those areas with high edge strength or edge density, i.e. text candi- dates. Garcia et al. [10] proposed a connected component based method in which potential areas of text are detected by en- hancement and clustering processes, considering most of the constraints related to the texture of words. Then, classification and binarization of potential text areas are achieved in a single scheme performing color quantization and characters peri- odicity analysis. Lienhart et al. [11] and Agnihotri et al. [8] are also proposed connected component based approaches. Alain Trémeau et al. [14] have proposed a method for detection and segmentation of text layers in complex images, which uses a geodesic transform based on a morphological reconstruction technique to remove dark/light structures connected to the borders of the image and to emphasize on objects in center of the image and used a method based on difference of gamma functions approximated by the Generalized Extreme Value Distribution (GEVD) to find a correct threshold for binariza- tion. Jianqiang Yan et al. [15] have used Gabor filters with scale and direction varied to describe the strokes of Chinese characters for target text area extraction and by establishing four sub-neural networks to learn the texture of text area, the learnt classifiers are used to detect target text areas. Existing methods experience difficulties in handling texts with various contrasts or inserted in a complex background. In this paper, we propose a connected component based text extraction algorithm, a general-purpose method, which can quickly and effectively localize and extract text from both document and indoor/ outdoor scene image. 2 PROPOSED METHOD In our proposed method we consider that text present in im- ages is in the horizontal direction with uniform spacing be- T