DOI : https://doi.org/10.18517/ijaseit.15.3.12396

Honey Badger Freeman Chain Code (HB-FCC) Feature Extraction Algorithm for Handwritten Character Recognition

Muhammad Arif Mohamad (1), Zuriani Mustaffa (2), Muhammad Faizan (3), Shahdatunnaim Azmi (4), Nor Amalina Mohd Sabri (5)
(1) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia
(2) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia
(3) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia
(4) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
(5) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
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[1]
M. A. Mohamad, Z. Mustaffa, M. Faizan, S. Azmi, and N. A. Mohd Sabri, “Honey Badger Freeman Chain Code (HB-FCC) Feature Extraction Algorithm for Handwritten Character Recognition”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 3, pp. 738–745, Jun. 2025.
Citation Format :
This study proposed the Honey Badger Freeman Chain Code (HB-FCC) feature extraction algorithm, an approach for feature extraction for Handwritten Character Recognition (HCR). HCR is a critical area of pattern recognition; to make it effective with minimal error and time consumption, the method of feature extraction must be robust enough. Traditional methods using the FCC have been proven efficient, but several drawbacks, including sensitivity to noise, dependence on initial conditions, and computational complexity, also limit them. To address the challenges mentioned above, this study proposes an improved FCC method utilizing the Honey Badger Algorithm (HBA), a recently developed metaheuristic optimization approach. The proposed HB-FCC algorithm focuses on optimizing the process of chain code generation using converted character images to graphs, where HBA is applied to minimize the length of FCCs and enhance recognition performance. The proposed method, in this regard, involves transforming a binary image of a handwritten character into a graph by representing the key points in a character's structure as nodes and the connections between these points as edges. These feature points are thus ranked according to the best paths identified by the HBA, thereby reducing the computational load and increasing the resilience of the feature extraction process against variations in handwriting and noise. The proposed HB-FCC feature extraction algorithm was further tested using a CEDAR dataset of handwritten characters. It demonstrated significant improvements in both computational time and route length compared to conventional FCC methods. This fact indicates the potential of HB-FCC in enhancing accuracy and efficiency in the HCR system.

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