A Segmentation-Recognition Approach with a Fuzzy-Artificial Immune System for Unconstrained Handwritten Connected Digits

Abstract

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.    

Author Biographies

Farou Brahim, LabSTIC Laboratory

Computer Science Department

Seridi Hamid, LabSTIC Laboratory

Computer Science Department

Authors

  • Merabti Hocine LabSTIC Laboratory
  • Farou Brahim LabSTIC Laboratory
  • Seridi Hamid LabSTIC Laboratory

Downloads

Published

10/25/2017

How to Cite

Hocine, M., Brahim, F., & Hamid, S. (2017). A Segmentation-Recognition Approach with a Fuzzy-Artificial Immune System for Unconstrained Handwritten Connected Digits. Informatica, 42(1). Retrieved from https://www.informatica.si/index.php/informatica/article/view/1067

Issue

Vol. 42 No. 1 (2018)

Section

Regular papers

License

Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.

All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.

Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.