Adaptive color document image binarization for text retrieval

Yi Li; Zhiyan Wang; Haizan Zeng

doi:10.1117/12.522033

15 December 2003 Adaptive color document image binarization for text retrieval

Yi Li, Zhiyan Wang, Haizan Zeng

Proceedings Volume 5296, Document Recognition and Retrieval XI; (2003) https://doi.org/10.1117/12.522033
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

This paper presents a decision tree based adaptive binarization method for text retrieval in color document images. This method extends Ni-Black windowed thresholding technique and hue (H), saturation (S) and value (V) are employed. First, an observation window is retrieved, and based on standard deviation of H, S and V, a pre-defined decision tree is used for selecting proper variables that should be employed. Secondly, Karhunen-Loeve Transform (KLT) is used for eliminating correlation and reducing dimension. Finally, center point of the window is classified based on 2-D standard normal distribution. The result shows that our binarization method generates better result than Ni-Black and other global thresholding binarization method such as Otsu’s in color document images. A comparison using a commercial OCR system shows that our method can be used in various situations for high quality text retrieval.

Citation Download Citation

Yi Li, Zhiyan Wang, and Haizan Zeng "Adaptive color document image binarization for text retrieval", Proc. SPIE 5296, Document Recognition and Retrieval XI, (15 December 2003); https://doi.org/10.1117/12.522033

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