## 5 Conclusions  
In conclusion, the proposed features give astounding results with ensemble classifiers on datasets free from any background complexity and ones containing distinguishable and uniform hand gestures, given that the right count of principal components is chosen for the features employing convolutional layers. The two datasets which deviate from obtaining appreciable accuracies from the majority of these features are the NUS I and II datasets comprising inter-class similarities and complicated environments, respectively. In this case, only the hand coordinates feature is able to maximally represent the sign images. The aforementioned anomalous datasets also perform well when trained with a standard CNN. In addition, the hybrid ANN has proved to be highly effective for sign gesture recognition by taking a combination of features from both the actual image and the image with the hand edges. In short, the research has been successful in constructing a hand gesture recognition system with the constraint that only with the help of hand coordinates alone can user-specificity be enforced which refers to the application of this system beyond the scope of the training data provided. This can be observed in the case of the ASL finger-spelling dataset which contains different user implementations for the same alphabet. Improvement can be made in this scenario by training a multitude of different sign image datasets captured under different conditions on a deep-tuned CNN. Currently, the entire system can operate on static sign gestures, i.e. images, so changes need to be made in this regard to capture real-time sign language usage that includes natural language rules. Conceptualization and methodology, A.J. and S.S.K. ; software, A.J. ; formal analysis, S.S.K. ; investigation, A.J. ; resources, S.S.K. ; data curation, A.J. ; writing--original draft preparation, A.J. ; writing--review and editing, S.S.K. ; visualization, A.J. ; supervision, S.S.K. ; funding acquisition, S.S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by Vellore Institute of Technology, Chennai. Not applicable. The datasets used in this research are publicly available in the following links: ASL alphabet: [[https://www.kaggle.com/datasets/grassknoted/asl-alphabet](https://www.kaggle.com/datasets/grassknoted/asl-alphabet)]([https://www.kaggle.com/datasets/grassknoted/asl-alphabet](https://www.kaggle.com/datasets/grassknoted/asl-alphabet)), accessed on 17 May 2022; ASL fingerspell dataset: [[https://empslocal.ex.ac.uk/people/staff/np331/index.php?section=FingerSpellingDataset](https://empslocal.ex.ac.uk/people/staff/np331/index.php?section=FingerSpellingDataset)]([https://empslocal.ex.ac.uk/people/staff/np331/index.php?section=FingerSpellingDataset](https://empslocal.ex.ac.uk/people/staff/np331/index.php?section=FingerSpellingDataset)), accessed on 17 May 2022; Cambridge hand gesture dataset: [[https://labicvl.github.io/ges_db.htm](https://labicvl.github.io/ges_db.htm)]([https://labicvl.github.io/ges_db.htm](https://labicvl.github.io/ges_db.htm)), accessed on 17 May 2022; NUS I Dataset: [[https://scholarbank.nus.edu.sg/handle/10635/137241](https://scholarbank.nus.edu.sg/handle/10635/137241)]([https://scholarbank.nus.edu.sg/handle/10635/137241](https://scholarbank.nus.edu.sg/handle/10635/137241)), accessed on 17 May 2022; NUS II Dataset: [[https://scholarbank.nus.edu.sg/handle/10635/137242](https://scholarbank.nus.edu.sg/handle/10635/137242)]([https://scholarbank.nus.edu.sg/handle/10635/137242](https://scholarbank.nus.edu.sg/handle/10635/137242)), accessed on 17 May 2022; ISL digits: [[https://www.kaggle.com/datasets/kartik2112/indian-sign-language-translation-letters-n-digits](https://www.kaggle.com/datasets/kartik2112/indian-sign-language-translation-letters-n-digits)]([https://www.kaggle.com/datasets/kartik2112/indian-sign-language-translation-letters-n-digits](https://www.kaggle.com/datasets/kartik2112/indian-sign-language-translation-letters-n-digits)), accessed on 17 May 2022.