Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Table 3

Comparative analysis of Braille to natural language conversion using Naïve Bayes, DT, SVM, KNN, sequential model, and GoogLeNet model.

Deep learning scheme for character prediction with position-free touchscreen-based Braille input method [33]
Classification techniques used	TPR (%)	TNR (%)	PPV (%)	NPV (%)	TA (%)	FPR (%)
Naïve Bayes	NaN	96.64	NaN	99.38	96.38	3.36
DT	NaN	98.82	NaN	98.23	97.20	1.18
KNN	NaN	98.60	NaN	98.11	97.04	1.40
SVM	68.67	99.16	76.75	98.72	83.00	0.84
Sequential model	90.76	99.63	91.07	99.2	92.21	0.36
GoogLeNet model	95.89	99.83	96.61	99.83	95.8	0.16
Detection of Urdu Braille characters based on reconstruction independent component analysis (RICA) features using robust machine learning techniques
DT	90.98	99.78	91.21	99.78	99.57	0.22
KNN	90.2	99.72	89.75	99.77	99.5	0.28
SVM	93.96	99.85	94.51	99.87	99.73	0.15