Decompose-threshold approach to handwriting extraction in degraded historical document images

Historical documents contain important and interesting information. A number of techniques have previously been proposed for thresholding document images. In this paper a new thresholding structure called the decompose-threshold approach is proposed and compared against some existing global and local algorithms. The proposed approach is a local adaptive analysis method, which uses local feature vectors to find the best approach for thresholding a local area. Appropriate algorithm(s) are selected or combined automatically for specific types of document image under investigation. The original image is recursively broken down into sub-regions using quad-trees until an appropriate thresholding method can be applied to each of the sub-region. The algorithm has been evaluated by testing on 10 historical images obtained from the Library of Congress. Evaluation of the performance using 'recall' value demonstrates that the approach outperforms any existing single methods.