Morphological preprocessing method to thresholding degraded word images
Pattern Recognition Letters
An algorithm for image binarization based on adaptive threshold
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
An intelligent method to extract characters in color document with highlight regions
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Display text segmentation after learning best-fitted OCR binarization parameters
Expert Systems with Applications: An International Journal
A robust text segmentation approach in complex background based on multiple constraints
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
The identification and filtering of fertilized eggs with a thermal imaging system
Computers and Electronics in Agriculture
Intelligent moving objects detection via adaptive frame differencing method
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Fast Circular Arc Segmentation Based on Approximate Circularity and Cuboid Graph
Journal of Mathematical Imaging and Vision
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This paper presents a novel binarization algorithm for color document images. Conventional thresholding methods do not produce satisfactory binarization results for documents with close or mixed foreground colors and background colors. Initially, statistical image features are extracted from the luminance distribution. Then, a decision-tree based binarization method is proposed, which selects various color features to binarize color document images. First, if the document image colors are concentrated within a limited range, saturation is employed. Second, if the image foreground colors are significant, luminance is adopted. Third, if the image background colors are concentrated within a limited range, luminance is also applied. Fourth, if the total number of pixels with low luminance (less than 60) is limited, saturation is applied; else both luminance and saturation are employed. Our experiments include 519 color images, most of which are uniform invoice and name-card document images. The proposed binarization method generates better results than other available methods in shape and connected-component measurements. Also, the binarization method obtains higher recognition accuracy in a commercial OCR system than other comparable methods