A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters

Danveer Rajpal, Akhil Ranjan Garg, Om Prakash Mahela, Hassan Haes Alhelou and Pierluigi Siano
Additional contact information
Danveer Rajpal: Department of Electrical Engineering, Faculty of Engineering, J.N.V. University, Jodhpur 342001, India
Akhil Ranjan Garg: Department of Electrical Engineering, Faculty of Engineering, J.N.V. University, Jodhpur 342001, India
Om Prakash Mahela: Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, India
Hassan Haes Alhelou: Department of Electrical Power Engineering, Tishreen University, Lattakia 2230, Syria
Pierluigi Siano: Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano, Italy

Future Internet, 2021, vol. 13, issue 9, 1-26

Abstract: Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

Keywords: bi-orthogonal; DCNN; DWT; Hindi characters; hybrid-features; fusion; MLP; PCA; SVM; transfer learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/9/239/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/9/239/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:9:p:239-:d:638581

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().