International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 12 | Dec 2025
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Communication Software for the Hearing Impaired Dr. Archana Ratnaparkhi, Anjanay Gangrade, Anay Gangshettiwar, Atharv Deshpande Professor, Dept. of ENTC Engineering, Vishwakarma Institute of Technology, Pune, India 2nd Year, Dept. of ENTC, Vishwakarma Institute of Technology, Pune, India 2nd Year, Dept. of ENTC, Vishwakarma Institute of Technology, Pune, India 2nd Year, Dept. of ENTC, Vishwakarma Institute of Technology, Pune, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Besides verbal communication, gestures play a crucial role in conveying messages. This paper outlines a real
time system implemented in Python capable of detecting hand gestures and translating them into corresponding letters of the alphabet. OpenCV handles video capture, while hand tracking is performed using Media Pipe. A CNN is employed to analyse the captured hand movement data. This system prioritizes speed and efficiency to ensure the solution is viable for interpreting and translating access sign language. This technology facilitates self-expression, thereby enhancing everyday conversations and improving communication for the users.
Key Words: Gesture recognition, sign language translation, assistive technology, hand tracking, computer vision, Open CV, Media Pipe, convolutional neural network (CNN), machine learning, accessibility
1. INTRODUCTION For the entire world’s people, communication is pivotal; however, those affected by listening and speech disabilities face significant challenges and barriers in their attempts at communication. The following outlines, in an effort to reach inclusivity, the development of a real-time gesture recognition system Developed with the user-friendly, flexible Python and its plentiful libraries, the system makes use of Media Pipe’s cap abilities in real-time hand tracking, and hand gesture movement analysis. OpenCV enhances user interactivity and experience in varied image processing tasks including video recording. Furthermore, image recognition trained CNNs provide precise gesture classification. The use of these methods, the system provides a quicker and more reliable means of communication. This can be improved upon in its daily use. In the modern world, where self and professional relationships are of utmost importance, the system can be leveraged to promote inclusivity for those hard of hearing or speech.
2. LITERATURE REVIEW There have been new developments which enhance the ac curacy of translating sign language, especially the conversion of sign language gestures into text. One such example uses convolutional neural networks (CNNs) for sign language gesture translation into text, illustrating how automated translation of gestures can be achieved through image preprocessing, key obtaining, and machine learning. By distinguishing spatial features, CNNs perform well in identifying number and letter signs and in some cases do better than a fully manual process. However, some important issues remain with dynamic gestures and continuous signing. This explains why the addition of RNNs or LSTMs for temporal regulation in the hybrid models will enhance system functionality significantly. Our objective is to translate sign language into text in real time using webcams. In this regard, we combine Open CV with Media Pipe and integrate neural networks to accomplish sign-to-text conversion and, later, speech synthesis. Tracking hand motions allows us to identify detailed movements in sign language. We show that deep learning techniques can be utilized in gesture recognition, yet issues like varying illumination and complex backgrounds in the environment pose challenges We suggest incorporating data augmentation to improve performance, as well as real-time adaptive thresholding and filtering to counter variable conditions. In addition to gesture recognition, there are multi-modal conversational systems designed for inclusive communication with the deaf and non-signers. A considerable amount of integration 3.
METHODOLOGY/EXPERIMENTAL 3.1 System requirements For this task to be executed effectively, certain software and hardware components will be needed. To accelerate training, the setup comprises a computer with a GPU and a webcam for live video capture. On the software side, Python 3.X along with required libraries, Media Pipe for palm tracking, OpenCV for video pro cessing, and Tensor Flow/Kara’s for deep
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