Extraction of handwritten data from noisy gray-level images using a multiscale approach
This paper presents a new methodology to extract visual data contained in noisy gray-level images such as mail envelopes. Since the intensity changes may occur over a large range of spatial scales in these and other like images, we adopt the multiscale approach to extract good quality data that might be used in further processing and recognition processes. We have already shown in a previous paper its effective use in full data extraction. In this paper, we will give an advanced formalism of this result, referred to as a top-down approach. Then, we will present a new and opposite approach, referred to as a bottom-up approach. The differences and characteristics of both approaches are highlighted. Experiments have been conducted on real life data from the data base provided by CEDAR at SUNY Buffalo, to assess the effectiveness of the proposed paradigm; this reveals its improved robustness and accuracy over the top-down approach; such a result might be useful for a wide range of applications in the held of image processing and enhancement.
image segmentation multi-scale concept and methodology handwritten data segmentation and recognition document processing and analysis