A multi-resolution based approach for handwriting segmentation in gray-scale images
We present a new method to segment visual handwritten data in gray-scale images. In handwriting recognition, visual shapes are very important in improving the system's performance. We introduce a robust method for extracting visual shapes of handwritten data from a noisy background. We adopted a multi-resolution Marr-Hildreth (1980) based approach to correctly segment visual data in variable contrasted images. Encouraging results have been obtained on real data, from the CEDAR database.<<ETX>>