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- Title
A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters.
- Authors
Rajpal, Danveer; Garg, Akhil Ranjan; Mahela, Om Prakash; Alhelou, Hassan Haes; Siano, Pierluigi
- 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%.
- Subjects
INDIA; PATTERN recognition systems; PRINCIPAL components analysis; SUPPORT vector machines; MACHINE learning; MUNICIPAL services
- Publication
Future Internet, 2021, Vol 13, Issue 9, p239
- ISSN
1999-5903
- Publication type
Article
- DOI
10.3390/fi13090239