Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touched or overlapped characters may be lost in many cases. If we analyze gray-scale images, however, specific topographic features and the variation of intensities can be observed in the character boundaries. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted from the gray-scale images. Then a nonlinear character segmentation path in each character segmentation region is found by using multi-stage graph search algorithm. Finally, in order to confirm the nonlinear character segmentation paths and recognition results, a recognition-based segmentation method is adopted. Through the experiments with various kinds of printed documents, it is convinced that the proposed methodology is very effective for the segmentation and recognition of touched and overlapped characters.

Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touching or overlapping characters may be lost. If we analyze gray-scale images, however, specific topographic features and the variation of intensity can be observed in the character boundaries. We believe that such kinds of clues obtained from gray-scale images should be useful for efficient character segmentation. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted form gray-scale images. Then the nonlinear character segmentation path in each character segmentation region is found by using multistage graph search algorithm. Finally, in order to confirm the character segmentation paths and recognition results, recognition based segmentation method is adopted.