Detection and recognition of text superimposed in complex background has been considered as a challenging problem. Most of the existing methods first locate the text regions and then feed them into OCR package for recognition. However, these methods cannot achieve good recognition performance due to the complex background. For this purpose, this paper proposes a novel text detection and recognition method by using color clustering to divide images into multiple layers according to main color class. In the proposed method, we exploited a connected component analysis to obtain the candidate text regions from each color layer, and then a cascade Adaboost classifier is adopted to determine whether the candidate text regions is real text regions in the corresponding image layer. Because the monochrome color exists in each layer, the interference of the background can be effectively reduced, which can significantly improve the accuracy of text regions localization. Afterwards, an OCR package is used to recognize the text regions which have been located by the cascade Adaboost classifier. Since the text region has a monochrome color, it helps to greatly improve the recognition rate. Finally, the relationship between different layers is used to verify the recognition results by the text location. The experimental results show that the proposed approach significantly outperforms the existing methods.

EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Main/Exploration

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{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     Pascal:14-0131436
   |texte=   Detection and recognition of text superimposed in images base on layered method
}}