CHARACTER RECOGNITION AND DETECTION USING CLUSTERING AND RNN METHOD FOR VISUALLY IMPAIRED
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Description
Text detection in images is an important step to achieve multimedia content retrieval. In this paper, an efficient algorithm which can automatically detect, localize and extract horizontally aligned text in images with complex backgrounds is presented. The proposed approach is based on the pre-processing, a method for edge detection, grouping of similar data using clustering and classified using the RNN classifier. The text contains vital and useful information which is embedded in various types of documents and natural scene. The extraction of text from a natural image is a challenging task. The text is detected and is extracted in this way that it readable by another person without any difficulty. In proposed a fast text localization method in which sobel edge detection and clustering method is used to localize all the possible edges in the image.After clustering Morphological process is done to identify exactly the character.After identifying the character it is segmented using basic segmentation technique.The RNN classifier is used for the comparison between the trained datasets and the segmented text.After comparison, the text been detected and the trained dataset character is obtained to be same then the corresponding audio file is been given as a output.
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CHARACTER RECOGNITION AND DETECTION USING CLUSTERING AND RNN METHOD FOR VISUALLY IMPAIRED.pdf
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References
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