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Research Article Open Access
Text in images contain important contents for information indexing and retrieval, automatic annotation and structuring of images. Hence text extraction is the crucial stage of analyzing the images. The steps involved in text extraction algorithms are detection, localization, binarization, extraction, enhancement, and recognition of text from the image. Text extraction is a very challenging task due to the variations in text size, font, style, orientation and alignment as well as complex background. Several text extraction techniques based on edge detection, connected component analysis, morphological operators, wavelet transform, texture features, neural network etc. have been developed. This paper provides a review of the various techniques suggested by researchers and their comparative analysis in terms of precision rate, recall rate, detection rate etc.
Discrete wavelet transform, Connected Component, Edge, Support vector machine, Discrete cosine transform., Extraction Chromatography