A Segmentation-Recognition Approach with a Fuzzy-Artificial Immune System for Unconstrained Handwritten Connected Digits
In this paper, we propose an off-line system for the segmentation and recognition of the unconstrained handwritten connected digits. The proposed system provides new segmentation paths by finding two types of structural features. The background and foreground features points are found from the input string image. The possible cutting paths are generated from these features points. Each candidate component is evaluated individually based on its features points and its height. The output of the segmentation module is evaluated using the fuzzy-artificial immune system (Fuzzy-AIS). The latter performs a decision function on the resulting segments, and then the hypothesis that has the best score is regarded as the global decision. The experimental results on the well-known handwritten digit database NIST SD19 show the effectiveness of the proposed system compared with other methods in both segmentation and recognition.
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