A GA based hierarchical feature selection approach for handwritten word recognition

  • Samir MalakarEmail author
  • Manosij Ghosh
  • Showmik Bhowmik
  • Ram Sarkar
  • Mita Nasipuri
Original Article


Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.


Hierarchical feature selection Genetic Algorithm Handwritten city name Bangla script Elliptical feature Gradient-based feature 



We would like to thank CMATER research laboratory of the Computer Science and Engineering Department, Jadavpur University, India, for providing us the infrastructural support. This work is partially supported by the PURSE-II and UPE-II, Jadavpur University projects. Showmik Bhowmik is thankful to Ministry of Electronics and Information Technology (Me-itY), Govt. of India, for providing him PhD-Fellowship under Visvesvaraya PhD scheme. Ram Sarkar is partially funded by DST grant (EMR/2016/007213).

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceAsutosh CollegeKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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