Name
Title | ||
|---|---|---|
A Fusion-Based Hybrid-Feature Approach For Recognition Of Unconstrained Offline Handwritten Hindi Characters |
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%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/fi13090239 | FUTURE INTERNET |
Keywords | DocType | Volume |
Bi-orthogonal, DCNN, DWT, Hindi characters, hybrid-features, fusion, MLP, PCA, SVM, transfer learning | Journal | 13 |
Issue | Citations | PageRank |
9 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danveer Rajpal | 1 | 0 | 0.34 |
| Akhil Ranjan Garg | 2 | 0 | 0.34 |
| Om Prakash Mahela | 3 | 4 | 4.46 |
| Hassan Haes Alhelou | 4 | 8 | 13.42 |
| Pierluigi Siano | 5 | 271 | 61.21 |