# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 5 Conclusions  
In conclusion, our study evaluated the performance of various classification algorithms and pre-processing techniques for recognizing hand gestures in Indian Sign Language. Our findings suggest that the SVM algorithm achieved the highest accuracy but required more computation time, while the naive Bayes model was faster but had lower accuracy. Implementing the SURF feature extraction technique significantly improved the accuracy of all classification models--naive Bayes, logistic regression, K nearest neighbors, and support vector machine, except for the CNN, which did not show any improvement because they are designed to extract features automatically from the input data, and SURF, which is an external feature extraction technique that is not of any help here. Our study also showed  
Figure 14: Output screen of gesture-to-text conversion successfully predicting G correctly.  
Figure 12: Recognition rate difference for alphabets using SVM with SURF and CNN without SURF.  
Figure 13: Output screen of gesture-to-text conversion successfully predicting 1 correctly.  
that pre-processing techniques such as skin masking, Canny edge detection, and feature extraction with SURF contributed significantly to the accuracy of the classification models by improving the quality of the input data.  
Regarding recognition rate consistency, the SVM algorithm with SURF outperformed the CNN model without SURF. Generating a consistent model is crucial for developing more efficient and accurate sign language recognition systems, which can greatly improve communication for people with hearing or speech impairments. Our gesture recognition system achieved word accuracy of 88 percent for alphabets and number correctness of 96 percent using the SVM classification model, which produced satisfactory results in real time. Overall, the findings of our study can guide the development of more effective sign language recognition systems that can facilitate communication for individuals with hearing or speech impairments.  
Finally, this study was an endeavor to contribute to making these amazing people's lives better by reducing the challenges they experience. Despite the study's early success, much more development may still be achieved in this area. We suggest exploring other feature extraction techniques and algorithms to further improve the accuracy and efficiency of the gesture recognition system. Additionally, we recommend investigating the system's robustness to different lighting conditions and camera angles to ensure its reliability in real-world applications.  
Conceptualization, P.K. and S.P.; methodology, P.K. and K.M.T.; software, K.M.T., R.J., P.K. and S.P.; validation, K.K. and S.A.; formal analysis, R.J. and P.K.; investigation, S.P. and K.K.; resources, P.K. and K.K.K.; data curation, S.A. and R.J.; writing--original draft preparation, K.M.T., R.J., P.K. and S.P.; writing--review and editing, K.M.T., P.K. and R.J.; visualization, S.P.; supervision, S.A.; project administration, P.K.; funding acquisition, K.K. and P.K. All authors have read and agreed to the published version of the manuscript.  
The APC was funded under the Research Support Fund of Symbiosis International (Deemed) University.  
Not applicable.  
The authors declare no conflict of interest.