MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085337 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be; and Malla Reddy Vishwavidyapeeth Deemed on July 13, 2026, for Sign Language Recognition – Word Formation And Text To Speech Conversion Using Machine Learning.

Inventors include Dr. Y. Madhaveelatha; Dr. Srinivas Yadlapaty; Ms. Hoyala Jataboina; Ms. Koppula Sireesha; Mr. Dondapati Thanesh; Dr. Yawer Abbas Khan; Dr. K. Jyothi; and Dr. Selvamani Indrajith.

The application for the patent was published on July 17, 2026, under issue no. 29/2026.

Abstract: Sign language is an important communication method used by people who are deaf or have speech disabilities. However, many individuals in society do not understand sign language, which often leads to communication difficulties between sign language users and non-users. To overcome this challenge, the proposed system presents a machine learning-based solution that can identify sign language gestures and convert them into spoken words. The system works in two major stages: gesture recognition and speech generation. In the first stage, computer vision techniques are used to capture hand gestures through a webcam or camera. A deep learning model, specifically a Convolutional Neural Network (CNN), is trained using a dataset of sign language alphabets and commonly used gestures. The system analyzes the captured video frames to detect hand features such as position, shape, and key points. These features are then processed by the trained model to recognize the corresponding gesture in real time and convert it into text. In the second stage, the generated text is transformed into speech using a Text-to-Speech (TTS) engine such as pyttsx3 or Google Text-to-Speech. This allows the system to produce clear voice output, enabling effective communication between sign language users and others. By combining gesture recognition and speech synthesis, the system acts as an assistive technology that improves accessibility and promotes inclusive communication.

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