# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 4 Results and Discussion  
Our study evaluated the performance of several classification algorithms (naive Bayes, logistic regression, K nearest neighbors, support vector machine, and convolutional neural network) in recognizing hand gestures for Indian Sign Language translation. Figures 10 and 11 can be observed to compare the accuracy achieved by each algorithm, with SVM achieving the highest accuracy but requiring more computation time and naive Bayes having lower accuracy but being faster. This study also evaluated the effectiveness of various pre-processing techniques, such as skin masking, Canny edge detection, and feature extraction with SURF. We found that these techniques contributed significantly to the accuracy of the classification models, improving the system's overall performance. It became very fruitful for us to switch from SIFT to SURF as it significantly sped up processing. It can also be well observed in Table 2 that our pre-processing techniques performed better in improving the quality of the input data by reducing noise, enhancing contrast, and normalizing the illumination of the images. Using the correct pre-processing tools and techniques is essential to ensure that the input data are consistent and reliable. If we analyze Figures 10 and 11, implementing the SURF feature extraction technique improved the accuracy of all the classification models. Naive Bayes, logistic regression, K nearest neighbors, and support vector machine showed a significant increase in accuracy when using SURF, with K nearest neighbors showing an increase of 27%. Convolutional neural networks, on the other hand, did not show any improvement in accuracy with the  
Figure 9: Confusion matrix. use of SURF. The naive Bayes model showed the lowest accuracy without SURF, with only 39%, while K nearest neighbors showed the highest accuracy without SURF, 62%. However, after implementing the SURF technique, the naive Bayes model showed a drastic improvement in accuracy to 82%, which is even higher than the accuracy achieved by K nearest neighbors without SURF. This suggests that the naive Bayes model highly depends on feature extraction and can greatly benefit from using techniques such as SURF. The logistic regression and support vector machine models also showed a significant improvement in accuracy with SURF, with an increase of 24% and 33%, respectively. This indicates that these models are also highly dependent on the feature extraction process, and  
Figure 11: Accuracy of models with using SURF. Figure 10: Accuracy of models without using SURF. techniques such as SURF can significantly enhance their performance. K nearest neighbors showed the highest accuracy without using SURF but still showed a significant increase in accuracy after implementing the feature extraction technique. This indicates that K nearest neighbors is less dependent on feature extraction than the other models but can still benefit from it. Convolutional neural networks, on the other hand, did not show any improvement in accuracy with the use of SURF. This is because CNNs are designed to extract features automatically from the input data and may not require additional feature extraction techniques such as SURF. It can be observed from Figure 12 that the recognition rate of SVM using SURF is significantly better and consistent than for CNN without using SURF, which shows better results than CNN with using SURF, as observed earlier in Figures 10 and 11. It is important to generate a consistent model as it can help develop more efficient and accurate sign language recognition systems, thereby improving communication for people with hearing or speech impairments. The system will check the gesture from the database where the user has already designed and taught several gestures after the threshold has been set. In this example, just the taught motions appear in the findings, which were trained using Google Collaboratory. The output file is related to the display gesture software in the form of an H5 extension.