Text Extraction Using Component Analysis and Neuro-fuzzy Classification on Complex Backgrounds
This paper proposes a new technique for text extraction on complex color documents and cover books. The novelty of the proposed technique is that contrary to many existing techniques, it has been designed to deal successfully with documents having complex background, character size variations and different fonts. The number of colors of each document image is reduced automatically into a relative small number (usually below ten colors) and each document is divided into binary images. Then, connected component analysis is performed and homogenous groups of connected components (CCs) are created. A set of features is extracted for each group of CCs. Finally each group is classified into text or non-text classes using a neuro-fuzzy classifier. The proposed technique can be summarized into four consequent stages. In the first stage, a pre-processing algorithm filters noisy CCs. Afterwards, CC grouping is performed. Then, a set of nine local and global features is extracted for each group and finally a classification procedure detects document’s text regions. Experimental results prove the efficiency of the proposed technique, which can be further extended to deal with even more complex text extraction problems.
KeywordsText extraction Color reduction Connected component analysis Adaptive run length smoothing Pattern classification Neuro-fuzzy classifier
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