Table 7 Comparative performance evaluation of the PASLR-DDPFEM technique through ablation study against existing models.

From: Improving sign Language recognition system for assisting deaf and dumb people using pathfinder algorithm with representation learning model

Methodology

\(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\)

\(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\)

\(\:\varvec{S}\varvec{e}\varvec{n}{\varvec{s}}_{\varvec{y}}\)

\(\:\varvec{S}\varvec{p}\varvec{e}{\varvec{c}}_{\varvec{y}}\)

\(\:\varvec{F}{1}_{\varvec{S}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\)

ENN + GF + SE-Densenet (Without PFA Hyperparameter tuning process)

98.18

83.79

83.65

98.77

83.71

PASLR-DDPFEM (ENN with GF preprocessing and SE-DenseNet feature extraction and PFA hyperparameter tuning process)

98.80

84.44

84.42

99.38

84.42

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