Table 1.
State-of-the-art methods for offline Tamil handwritten recognition.
| Authors | Methods used | Performance | Drawbacks |
|---|---|---|---|
| Shyni et al.3 | CNN, MLP classifier | Overall, Accuray 88% | Low accuracy and high processing time due to the increase in the number of layers in the proposed model |
| Raj et al.2 | SVM |
Test accuracy- 95.2% Training accuracy-93.9% Validation accuracy-87.41% |
High Misclassification rate |
| Sasipriya et al.5 | SFGDO (Smart flower gradient decent optimisation based generative adversarial network) |
Accuracy-91.8% Sensitivity-92.5% Specificity-94% |
Time and space complexity perform well only with a larger number of samples |
| Vinotheni et al.6 | Deep CNN |
Accuracy-98.48% Precision- 98.38% Sensitivity- 97.98% Specificity- 98.27% F-measure- 98.35% |
Low recognition accuracy |
| Gayathri et al.7 | Transfer learning | Overall accuracy- 93.1% | Low accuracy, high training time |
| Jayanthi et al.8 | Bayesian optimisation algorithm, CNN |
Test Accuracy- 87.3% Training Accuracy- 96.9% |
Characters with similarities cannot be recognised correctly |
| Bavani et al.10 | MVGG16, VGG16 | Overall accuracy- 91.8% | Time complexity, low accuracy |
| Sasipriya et al.9 | Scrabble-GAN (Generative adversarial network and augmentation) with CNN | Overall accuracy- 96.23% | Unbalanced dataset, low accuracy, low precision |
| Sonthi et al.11 | Objective mayfly optimisation with deep learning (MOMFO-DL) |
Accuracy-99% Precision- 98.74% Recall-98.94% F-Score-98.48% |
Low accuracy |
| Suriya et al.12 | CNN | Average accuracy-97.7% | Can detect only partial Tamil alphabets (not trained with vowelized consonants) |
| Shaffi et al.13 | Unconstrained Tamil handwritten character database(uTHCD) |
Test Accuracy- 87.3% Validation accuracy-93.32% |
Unbalanced dataset, low segmentation accuracy |
| Kaur et al.14 |
Experimental analysis: k-NN, SVM, Random Forest Ensemble algorithm: AdaBoost |
Overall accuracy- 88.78% | Low accuracy, precision, and recall |
| Pandey et al.15 | CNN is using transfer learning | Overall accuracy- 97.67% | Unbalanced dataset |
| Hidayat et al.16 | CNN | Testing accuracy-97.74% | Low accuracy, low recognition rate |
| Wajih et al.17 | CS-LBCNN, TCS-LBCNN |
CS-LBCNN-99.50% TCS-LBCNN-99.54% |
Low accuracy, unbalanced dataset |
| Anjum et al.18 | CALTEXT |
For the Urdu dataset: Character recognition rate- 82.06% Word recognition rate- 51.97% For the Arabic dataset: Character recognition rate- 77.47% Word recognition rate- 37.66% |
Time complexity, Low accuracy |
| Raj et al.19 |
Z-ordering algorithm for addressing the structure and shape of locational features PM Quad tree |
Z-ordering algorithm-88.3% PM quad tree- 82.4% Average result-94.7% |
Misclassifications, Low accuracy |
| Kowsalya et al.20 |
ANN- feature extraction, Elephant herd algorithm- modifying weights |
Overall accuracy-92.52% | Low accuracy, low precision, low recall |