Script Identification from Printed Indian Document Images and Performance Evaluation Using Different Classifiers

Table 4

Statistical performance analysis and weighted average of the measuring parameters are shown here.

Classifier	KS	MAE	RAE	TP rate	FP rate	Precision	Recall	-measure
BayesNet	0.9631	0.007	3.9702	0.967	0.004	0.969	0.967	0.967
LibLINEAR	0.9729	0.0048	2.7033	0.976	0.003	0.977	0.976	0.976
MLP	0.9828	0.007	3.9239	0.985	0.002	0.985	0.985	0.985
RBFNetwork	0.9509	0.0101	5.6777	0.956	0.005	0.960	0.956	0.957
Simple Logistic	0.9877	0.0059	3.3448	0.989	0.001	0.989	0.989	0.989
FURIA	0.9277	0.0198	7.2849	0.941	0.013	0.941	0.941	0.940
PART	0.9065	0.0177	9.9616	0.917	0.010	0.917	0.917	0.916
NBTree	0.9312	0.0159	8.978	0.939	0.008	0.940	0.939	0.939
Random Forest	0.9729	0.0248	13.9833	0.976	0.003	0.976	0.976	0.975

Parameters considered: KS: Kappa statistics, MAE: mean absolute error, RAE: relative absolute error and TP rate, FP rate, precision, recall, -measure.