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

Author

Listed:
  • 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
    Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2193, South Africa)

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%.

Suggested Citation

  • Danveer Rajpal & Akhil Ranjan Garg & Om Prakash Mahela & Hassan Haes Alhelou & Pierluigi Siano, 2021. "A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters," Future Internet, MDPI, vol. 13(9), pages 1-26, September.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:239-:d:638581
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