Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{164738,
        author = {Ellanthakunta Sri Krishna Vamshi and Kalwala Nivas Reddy and Shaik Shhameem and P Sandhya Rani},
        title = {gestures to text converter},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2596-2600},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164738},
        abstract = {Engaging in effective communication with individuals who cannot speak presents challenges, primarily because the general populace may not be proficient in sign language. Gestures-to- Text Converter is an innovative system designed to interpret the gestures made by special people and subsequently translate these gestures into textual formats. The project's goal is to facilitate effective communication with special needs. The project involves creating a dataset comprising more than 36 distinct types of gestures, followed by the utilization of a Random Forest Classifier for gesture classification. RFC for gesture classification is a smart choice because it offers high accuracy, handles various types of gestures, identifies important features, and is robust against overfitting. The RFC is trained using the created dataset, enabling it to generate a textual representation of the corresponding gesture. The translation of gestures made by individuals with special needs into text facilitates effective communication with those who may not understand sign language but can comprehend written text. The gestures are then translated into text. The final output includes the textual representation. A mixed model is proposed based on previous existing approaches.},
        keywords = {Gesture Recognition, Sign Language Translation, Random Forest Classifier, Human-Computer Interaction, Communication Aid, Machine Learning, Assistive Technology, Non-Verbal Communication, Special Needs Accessibility, Feature Extraction, Gesture Dataset, Textual Representation, Real-time Translation, Data Classification, Gesture Interpretation, Sign Language Dataset, Overfitting Prevention, Gesture-to-Text System, Inclusive Communication, Multi-Class Classification},
        month = {},
        }