Research Article
Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques
Table 4
Comparative analysis with previous literatures.
| Supported languages | Techniques used | Feature extraction/algorithms/others | Accuracy (%) | References |
| Hindi | SVM | HOG features | 94.5 | [24] | Chinese | Image segmentation techniques | Image segmentation techniques | 79.63 | [52] | Chinese | DNN | DNN | 90.47 | [53] | Simple Braille recognition | MLP | SDAE | 86 | [34] | RBF | 80 | SoftMax | 92 | MLP | Traditional feature extraction | 64 | RBF | 55 | SoftMax | 65 | KNN | 63 | Naïve Bayes | 53 | Random forest | 65 | SVM | 69.6 | Tamil | Nil | Image segmentation technique | 99.2 | [28] | Hindi | 98.8 | Sinhala | SVM | HOG feature extraction method | 80 | [35] | Arabic | Newly designed Braille letter recognition and transcription scheme for Braille to Arabic text conversion | Image segmentation | 99 | [21] | Odia | SVM | HOG features | 99 | [36] | Korean | CNN model | ------ | 99.6 | [54] | Bangla | Deep neural network | VGG-16 | 94.62 | [55] | ResNet-50 | 93.58 | DenseNet-121 | 94.08 | Grade 1 Urdu Braille | Decision tree | RICA feature extraction method | 99.57 | Newly proposed coordinate-based model | KNN | 99.50 | SVM | 99.73 |
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